Total Result(s) Found: 164

Large-Scale Volumetric Mesh Visualization and Analysis
Academic Program: Computer Science
Very large volumetric meshes are of crucial importance in many areas such as large-scale computational fluid dynamics (CFD) simulations, whether for car engine design or for simulating oil and gas reservoirs. Recent computational advances have led to computational grids of extreme size, such as trillion-cell reservoir simulations. The size and complexity of such grids pose a tremendous challenge to interactive visualization and analysis, and require the development of novel data structures for visualization, e.g., polyhedral grid data structures, as well as data structures for efficient querying and analysis.
RGC/3/5205-01-01
ondrej.strnad@kaust.edu.sa
Visualization, volumetric meshes, polyhedral grids
Visualization
The oil reservoir visualization and simulation come with several challenges. One of them is to handle a typically large amount of data consisting of multiple attributes of a specific geological area. We want to explore the possibility of predicting these attributes in different but geodetically similar areas using machine learning algorithms. We expect our framework for oil reservoir visualization and simulation to be extended in the way that we will be able to incorporate different machine learning algorithms in the future.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Membrane development for sustainable separations
Academic Program: Chemical Engineering

The goal of this project is the development of multilayer polymeric membranes for liquid separation. The main target is the application in the pharmaceutical industry for the separation of complex mixtures of molecules with size smaller than 500 g/mol. The membranes are mainly prepared by interfacial polymerization as flat-sheet or hollow fibers.   The characterization methods are chromatography, electron microscopy, and performance tests mimicking operational conditions. There will be possibilities of scaling up the membranes with the best performance.

ASP/1/1669-01-01
suzana.nunes@kaust.edu.sa
Chemistry
Chemistry or Chemical Engineering
Thin-film composite membranes for liquid separation
Chemical Engineering
Biological and Environmental Sciences and Engineering
Undergraduate
Uncovering and Addressing Bias in LLM Interactions
Academic Program: Computer Science
Large Language Models (LLMs) have become ubiquitous in contemporary applications. They are trained on extensive collections of human writings, ranging from books, papers, and news articles to conversations on social media platforms. This comprehensive approach enables the development of sophisticated tools capable of emulating human interactions with remarkable fidelity. However, it is important to recognize that LLMs might inherit and perpetuate the biases inherent in human communications. This project represents a concerted effort to delve deeply into the multifaceted landscape of biases inherent in interactions with LLM agents. By examining various dimensions of biases, we aim to explore how these biases manifest within LLM-mediated interactions. Through this project, we would not only understand the complexities of bias within LLM interactions, but also explore the possibility to mitigate, neutralize, or rectify these biases. This will underline if LLMs are more inclined to change opinions than humans. This approach underscores our commitment to fostering fairness, equity, and inclusivity in the realm of LLM-driven communication and interaction, ultimately advancing the societal impact and ethical integrity of LLM technology.
BAS/1/1701-01-01
roberto.dipietro@kaust.edu.sa
LLM, moderation, bias, guardrail, security, privacy.
Computer Science
The project anticipates two primary outcomes: the creation of a system capable of simulating interactions within an online platform using LLMs as agents, and the utilization of the system to gain knowledge on the biases exhibited by LLMs across various operational scenarios.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Resilient Computing and Cybersecurity Center
Accelerated simulation of reactive flows using deep neural networks
Academic Program: Mechanical Engineering
Computational fluid dynamic (CFD) simulations of chemical reacting flows demand excessive computational time in order to solve a large number of highly nonlinear chemical reaction source terms. The project aims to develop an algorithm for accelerated computations by developing high fidelity reduced-order data-based chemical kinetics solver using autoencoder and neural network algorithm. The basic framework has been developed and the new project will apply the algorithm for renewable fuel applications over a wide range of thermodynamic conditions and assess the fidelity and performance enhancement of the new algorithm. The student will gain understanding of various modern machine learning tools in engineering applications with hands-on experience of programming and implementation.
BAS/1/1316-01-01
vijayamanikandan.vijayarangan@kaust.edu.sa
Machine learning, combustion modeling, computational fluid dynamics, reduced order modeling
Mechanical/aerospace engineering, computational modeling, machine learning
Implementation of the code modules, simulations and analysis of the results for assessment of fidelity and performance. Upon successful outcome, a conference and/or journal paper publication is expected.
Mechanical Engineering
Physical Sciences and Engineering
Clean Combustion Research Center
Unlocking the Secrets of the Extreme Microbiome: Biotechnological Frontiers on Earth and Beyond
Academic Program: BioScience
Extremophiles are specialized microorganisms that thrive under severe conditions, such as extreme temperatures, pH levels, and pressures. These organisms are fascinating subjects for scientific research and promising candidates for diverse biotechnological applications. Saudi Arabia's harsh environments, from deserts to volcanic areas, are home to these resilient organisms. These unique settings, resembling extraterrestrial landscapes, provide exceptional opportunities to leverage extremophiles for biotechnological innovations. Our project aligns with Saudi Vision 2030's goal of promoting sustainable economic growth. Our project seeks to tap into this potential by developing a multidisciplinary framework that combines advanced isolation techniques, omics technologies, and state-of-the-art facilities at KAUST. We aim to isolate novel microbial strains, explore our culture collection, and uncover their metabolic functions to pave the way for new biotechnological applications and enhance our understanding of life's adaptability. Our research extends to evaluating these extremophiles' potential to support sustainable goals on Earth and their implications in astrobiology and space research. Complementing this, our ongoing study of space-related microorganisms further expands the scope and impact of our research.
BAS/1/1096-01-01
junia.schultz@kaust.edu.sa
Microbiology, Extremophiles, Astrobiology, Biotechnology
Microbiology
-Isolation and Characterization of Novel Extremophile Species: Deliver a curated collection of newly isolated extremophile species from Saudi Arabia's unique environments, such as volcanic regions, hot springs, hydrothermal vents, and hyper-arid deserts, among others. This deliverable involves using advanced isolation techniques to culture these organisms, followed by genomic and phenotypic characterization to understand their unique adaptations and capabilities. We are aiming for further biotechnological exploration and contributing to the global extremophile database. -Functional Genomics and Metabolic Pathway Analysis: Conduct comprehensive omics analyses (genomics, transcriptomics, proteomics, metabolomics) to elucidate the metabolic pathways and biological functions that enable extremophiles to survive under extreme conditions. This deliverable aims to identify genes, proteins, and metabolites associated with resilience to extreme environmental stresses, which could be harnessed for biotechnological applications such as bioremediation, pharmaceuticals, or industrial enzymes. -Development of Biotechnological Applications and Sustainability Assessment: Utilize the data from the isolation and omics studies to develop biotechnological applications that leverage the unique properties of extremophiles. This could include engineering extremophilic enzymes for industrial processes that require high temperatures or acidic conditions or developing extremophile-based biofilters for environmental cleanup. Additionally, assess these applications' sustainability and economic viability in alignment with Saudi Vision 2030's objectives for economic diversification and environmental sustainability. This deliverable would also explore the potential implications of extremophile research in astrobiology and space exploration, enhancing our understanding of life's possibilities beyond Earth.
BioScience
Biological and Environmental Sciences and Engineering
Predicting Bacterial Antimicrobial Resistance Phenotypes from Genomic Biomarkers
Academic Program: BioScience
The Infectious Disease Epidemiology lab of Prof. Danesh Moradigaravand is looking for student interns to work on a range of projects on the intersection of machine learning and microbial genomics. We are interested in understanding the evolution and epidemiology of antimicrobial resistance strains recovered from natural sources, including clinical and environmental sites. Antimicrobial resistance is a global health threat, expected to become a leading cause of deaths worldwide within the next three decades. This is predominately due to the rapid emergence of novel genetic variants, which lead to new resistance mechanisms. The understanding of the genetic repertoire of resistance is henceforth important to design new antimicrobial strategies and develop new compounds. In this project, the student will develop a machine learning-based predictive model to systematically identify resistance determinants in bacterial genomes. The projects involves integration of genomic and phenomic data, generated by high throughput phenotypic assays using an off-the-shelf machine learning method. The student will implement an entire predictive pipeline and deploy the model as a data science solution. The project helps student gain hands-on experience of programming, next generation sequencing data analysis and machine learning
BAS/1/1108-01-01
danesh.moradigaravand@kaust.edu.sa
Microbial genomics, Machine Learning
Microbial genomics
Develop and deploy a machine learning pipeline Disclose a database of predictive biomarkers for antimicrobial resistance Contribute to relevant publications
BioScience
Biological and Environmental Sciences and Engineering
Computational Bioscience Research Center
Design and performance evaluation of various emerging 6G wireless communication technologies
Academic Program: Electrical Engineering
The advent of 6G wireless communication systems heralds a new era of connectivity, promising unprecedented data rates, ultra-low latency, and ubiquitous coverage. We have at the CTL lab @ KAUST various research projects aiming to investigate the integration of several emerging technologies such as Free Space Optics (FSO), High Altitude Platform Systems (HAPS), and Low Earth Orbit (LEO) mega constellations to enhance the performance and capabilities of 6G networks. By leveraging the unique characteristics of each technology, this study seeks to address the challenges of achieving high-speed, reliable, and secure wireless communication in diverse environments and scenarios. These research projects (mentored by postdocs in the CTL group) will employ a multi-faceted approach combining literature review, theoretical analysis, simulation studies, and sometimes experimental validation.
BAS/1612
slim.alouini@kaust.edu.sa
6G, Wireless Communication Systems, Free Space Optics (FSO), High Altitude Platform Systems (HAPS), Low Earth Orbit (LEO) Mega Constellations, Integration, Seamless Handover, Resource Management.
Wireless Communication
1- regular progress report 2- final report that can be further polished for journal submission at the end of the internship
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Explainable Artifical Intelligence Methods for Wellbore Damage Zone Prediction
Academic Program: Energy Resources and Petroleum Engineering
Despite the overwhelming interest in artificial intelligence, a key hurdle remains for the large-scale adoption of ML-solutions; this is the “black-box” nature of the predictions where a model returns a value but with no obvious reasoning or tracking process of how the value was obtained. In this project, we shall focus on looking inside the black-box and producing not only the model’s prediction but also the reasoning the model took to deliver a given predicted value. This project aims to address this challenge through the use of eXplainable Artificial Intelligence (XAI) methodologies. The selected student will get the opportunity to work at the forefront of artificial intelligence methods, identifying how models come up with the solutions they offer and utilising that to further enhance the methods. To illustrate the benefit of XAI, we will focus on the use case of interpreting natural and induced damage zones at the wellbore wall and in the near wellbore region (e.g., natural fractures, drilling-induced tensile fractures, wellbore breakouts) from electrical, optical and acoustic well image logs. Naturally occurring fracture and fault systems play a key role in governing subsurface flow. Often, major flow paths are associated with large faults and their damage zones. Thus, detailed characterization of pre-existing fault and fracture networks, in terms of orientation, intensity and aperture, is vitally important for several industrial applications, such as water supply, hydrocarbon production, geothermal energy production, radioactive waste management, and carbon capture and storage. Drilling induced damage (drilling-induced tensile fractures and stress-induced borehole breakouts) can be of primary importance to depict the subsurface in-situ stress orientation and magnitude and, thus, for an optimal planning of well trajectory and monitoring of the wellbore stability. The numerical analysis will begin by training multiple ML models to predict the presence of a damaged zone and whether it is an open or closed fault. A range of models will be considered from highly interpretable decision trees to highly complex convolutional neural networks. Numerous XAI components will be investigated to provide a reasoning behind each models’ predicted values. This reasoning will be compared against our knowledge of the geological systems, allowing us to analyse if there is a physical meaning behind the model. This aids our understanding if and when we can trust a model’s prediction and the value of using it. The workflow developed in this project will be directly applicable to all ML-assisted log interpretation algorithms. A key outcome from this internship is to provide us with a clear understanding of which ML method is most suitable for wellbore damage zone prediction from wellbore image logs.
BAS/1/1421-01-01
claire.birnie@kaust.edu.sa
Computer Vision; eXplainable AI; Machine Learning, wellbore damage zone interpretation
Engineering or Computer Science
1. Algorithm development with Python to import digitized wellbore image log and related damage zone interpretations; 2. Develop a ML model for damage zone identification, under guidance; 3. Investigate explainable AI methodologies to determine how the ML model arrives at a given prediction; 4. Analyse the XAI results and tie these results to physical interpretations made by petrophysicists; 5. Assist with manuscript write-ups for publication.
Energy Resources and Petroleum Engineering
Physical Sciences and Engineering
Ali I. Al-Naimi Petroleum Engineering Research Center
Learning to Cooperate in Multi-robot Task Allocation
Academic Program: Electrical Engineering
Imagine robots self-organizing into large groups to assist people with physically demanding tasks, leveraging their core capabilities in perception, manipulation, and navigation to interact with the physical world. To successfully complete a given mission as a team, these robots must make their own decisions to allocate and carry out tasks defined in the mission and cooperate with one another when the task requires it. We realize this capability in multirobot systems through a learning-based paradigm, designing computational models to train a large number of robots to work as a team. Achieving a high level of autonomy in distributed information processing, decision-making, and collaborative manipulation is crucial. To enable a high level of cooperation among a large number of robots, the project aims to build computational models based on deep reinforcement learning (DRL). These models are designed to enhance the robots' ability to cooperate in carrying out multiple tasks using their perception, manipulation, and navigation skills. Additionally, the project's goal is to implement these models on a multi-robot platform and validate the effectiveness of the models through lab experiments.
BAS/1/1695-01-01
shinkyu.park@kaust.edu.sa
Robotics, Machine Learning, Reinforcement Learning
Electrical and Computer Enginnering
The main objective of this project is to implement multi-agent DRL in a team of mobile manipulators and validate our DRL implementation through lab experiments. Students are expected to collaborate with lab members to explore new ideas and implement them on physical robotic systems. To fulfill the requirements of the project, students should have solid experience working with robotic systems and the Robot Operating System, as well as confidence in C++/Python programming. Experience with reinforcement learning is a plus. The model design and experiment reports are expected to be delivered at the end of the internship program.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Polkadot Graph Analysis
Academic Program: Computer Science
"Polkadot Graph Analysis" is an ongoing research project investigating the dynamics of a cryptocurrency network through transaction graph analysis. The project involves extracting and analyzing data from the Polkadot blockchain to understand transaction patterns, network structure, and potential anomalies. The contributions will span a broad spectrum: Beginning with the definition and categorization of transactions in the Polkadot network, the project's main goal is to design and build the full transaction graph to support further analysis rooted in sound graph theory. This analytical approach will allow the application of several metrics, including degree distribution, strongly/weakly connected components, density, and various centrality measures that will be useful in assessing many properties of the Polkadot network.
BAS/1/1701-01-01
roberto.dipietro@kaust.edu.sa
Graph Analysis, Cryptocurrencies, Security
Computer Science
The expected outcome of this project is twofold. The student is expected to investigate the complex Polkadot environment to categorize on-chain events and data. Secondly, the student will develop a novel tool to parse the Polkadot ledger efficiently and build the corresponding transaction graph.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Resilient Computing and Cybersecurity Center
Encrypted Traffic Classification
Academic Program: Computer Science
A notable trend in the rapidly evolving mobile technology domain is the increasing reliance on encrypted network packets to strengthen privacy and security. Nevertheless, certain unencrypted elements, such as packet size and other critical Internet functionalities, remain exposed despite this encryption. This project is dedicated to harnessing these aspects by developing a tool adept at classifying encrypted network packets. Utilising Deep Learning models, the tool is designed to categorise the traffic on a network and deduce the types of applications on mobile phones. Positioned at the intersection of network security and AI-driven analysis, this innovative project emphasises developing and refining cutting-edge deep-learning models. These models are specifically designed to pinpoint the applications generating network traffic despite encryption. This capability is made possible by identifying unique patterns and traits within the data flow indicative of specific applications installed on a smartphone. This project is committed to privacy, and ethical considerations are a vital aspect of this project. While encrypted packets bolster security, the ability of tools to categorise their contents and deduce installed applications poses privacy challenges. The project addresses these concerns by offering a solution that respects user privacy while yielding valuable insights into network traffic. Therefore, the project approaches these challenges with a solution that respects user privacy while providing valuable insights into network traffic. This balance of privacy with technological advancement sets a new standard in network traffic analysis, underscoring the project's innovative and conscientious approach.
BAS/1/1701-01-01
roberto.dipietro@kaust.edu.sa
Security, Encrypted traffic, Classification, Machine Learning
Computer Science
The expected outcome of this project is twofold. The student is expected to design an innovative tool that can efficiently categorise mobile traffic by application and deduce the specific applications installed on each smartphone. Secondly, the student will work closely with team members to develop this tool and comprehensively analyse the traffic data gathered.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Resilient Computing and Cybersecurity Center
Materials Engineering for Soil Amendment in Saudi Arabia
Academic Program: Environmental Science and Engineering
Our group has pioneered game-changing technology to couple the giga-scale challenge of organic landfill diversion with the giga-scale opportunity of desert agriculture and greening. We are producing engineered biochar (EnB) by pyrolyzing organic waste, such as chicken manure, crop residue, and food scraps, and subjecting it to a post-treatment. We are studying the effects of its application on food crops as well as native plants as a function of synthesis, post-treatment, dosing, nutrient loading, and variable irrigation regimes.
BAS/1/1070-01-01
lisa.okiexposito@kaust.edu.sa
sustainability, food-water security, desert rehabilitation,
materials engineering, food-water-climate security, organic landfill diversion
1) materials characterization - surface area, ion-exchange capacity, 2) application to plants - pot-scale and field work 3) monitoring plant health - yield, biomass growth (leaves, branches, stem, etc.), chlorophyll content, gas-exchange, etc. 4) analyzing results 5) scientific writing
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Modeling human early development with stem cell-based integrated embryo-like models
Academic Program: BioEngineering
Academic Program: BioScience
A peculiarity of human reproduction is the high rate of developmental failure before and after the time of embryo implantation. Understanding why early pregnancy failures occur is a significant question in the field but remains challenging due to the technical and ethical complexities involved in studying these developmental stages. Pluripotent stem cells have recently been used to construct various early developmental models that hold great promise in unlocking the mysteries of early human development and facilitating new reproductive therapies. Human blastoids are one such valuable model for studying early human development. They can be derived from naïve pluripotent stem cells and consist of all three lineages of the preimplantation blastocyst. Current methods using static batch culture to generate human blastoids have limitations, including (i) low yield, (ii) the need for cell aggregation, which complicates genetic or drug screens, (iii) reproducibility issues due to an uncontrolled cellular environment, and (iv) the requirement for Aggrewell or similar confinements that can exert unnatural geometrical strains on blastoids. Additionally, current blastoid models exhibit instabilities in the development of post-implantation lineages. The candidates of this VSRP project are expected to work on developing methods for large-scale production of human blastoids under strict environmental control, improving post-implantation lineage differentiation of blastoids, and characterizing gene expression in a spatially resolved manner in single cells. The successful outcome of the project is expected to significantly advance the development of in vitro human embryo models that faithfully recapitulate specific intricacies of embryogenesis. These models have the potential to be translated into applications in regenerative medicine, disease modeling, and personalized therapies.
BAS/1/1080-01-01
arun.chandrasekaran@kaust.edu.sa
blastoid, pluripotent stem cell, human development, reproductive biology, embryogenesis, regenerative medicine
stem cell, developmental biology, bioengineering
1. Developing methods for large-scale production of human blastoids under strict environmental control 2. Improving post-implantation lineage differentiation of blastoids 3. characterizing gene expression in blastoids in a spatially-resolved manner in single cells
BioEngineering
Biological and Environmental Sciences and Engineering
Parameter-free optimization, universal prediction, and Kolmogorov complexity
Academic Program: Computer Science
Online parameter-free optimization algorithms are optimization algorithms that do not require tuning of parameters, yet they provable achieve the optimal performance. The basic idea behind these algorithms is in the link between universal strategies for prediction with log loss and online convex optimization, first unveiled in Orabona&Pal (2016). By now, this line of work has produced a number of parameter-free optimization algorithms. However, universal prediction with log loss is also connected to the concept of Kolmogorov complexity for binary strings, as showed by Cover (1974). In this project, we aim at studying all these links, going from parameter-free optimization to Kolmogorov complexity, passing through universal prediction with log loss. We want to construct explicit reductions to transform each of these problem in one of the other ones. The final aim is to directly link convex optimization to an appropriate notion of computational complexity for functions. Moreover, we want to extend some of these concepts from binary strings to strings of bounded real numbers. Given the theoretical nature of this project, the ideal candidate must have an excellent mathematical background.
BAS/1/1704-01-01
francesco.orabona@kaust.edu.sa
parameter-free, optimization, Kolmogorov complexity
​Computer Science, Mathematics or a related discipline
Original research – contribution to a research paper​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Training dynamics of Adam
Academic Program: Computer Science
One of the most used algorithm to train deep neural networks is Adam. However, despite its empirical success, it is a poorly understood algorithm. In particular, existing mathematical theories fail to capture a quantifiable advantage over the classic stochastic gradient descent. In this project, we will take a different route: Instead of studying Adam as a black-box under simplified assumptions, we will carefully analyze its empirical training dynamics, in particular in the first iterations. We aim at pinpointing the key differences between the training dynamics of Adam and the ones of stochastic gradient descent with momentum. Later, using the gathered knowledge, we will formulate a mathematical model of its behavior.
BAS/1/1704-01-01
francesco.orabona@kaust.edu.sa
machine learning, optimization, Adam, SGD
​Computer Science, Mathematics or a related discipline
Original research – contribution to a research paper​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Coral Microbiology and probiotics
Academic Program: Marine Science
The use of Beneficial Microorganisms for Corals (BMCs) as coral probiotics is one new methods being explored for reef conservation, restoration and rehabilitation. Our group has proposed and proven the concept that BMCs can mitigate the impacts caused by thermal stress and pathogens (Rosado et al., 2019). Despite their documented success in protecting against coral bleaching, the mechanisms associated with this protection, its application, fate of inoculated microbes and success in natural systems, and BMC possible interactions or connectivity with other organisms in the reef, remain to be explored. For this reason, the intern will join current projects being currently developed by our group that aim to isolate, select, and assemble specific BMC consortia from the Red Sea coral reefs and evaluate its role in promoting coral growth, coverage, health and connectivity with other organisms as well as to perform a deep investigation of the symbiotic relationships between corals and their associated microbiota, and its ecological outcomes.
BAS/1/1095‐01‐01
helena.villela@kaust.edu.sa
coral probiotics, coral microbiology, microbiome stewardship, marine microbiomes
Marine Microbiology
Survey of molecular microbial-mediated mechanisms to promote coral health and growth.
Marine Science
Biological and Environmental Sciences and Engineering
Undergraduate
Red Sea Research Center
Natural Deep Eutectic Solvents for water remediation
Academic Program: Environmental Science and Engineering
Natural deep eutectic solvents (NADES) are composed of two or more natural compounds, such as organic acids and bases, sugars, amino acids, and polyalcohols that interact through hydrogen bonds, forming a stable liquid. Due to their components, these green solvents are more biodegradable, harmless, and environmentally friendly than similar solvents, such as ionic liquids and DES. In our research group, we focus on understanding the chemical and physical properties of NADES and their applications, e.g., biofouling cleaning solvents, wastewater pre-treatment agents, and liquid membrane carriers. In this project, the student will learn how to prepare and characterize green solvents and study their application in water remediation.
BAS/1/1087-01-01
andreia.farinha@kaust.edu.sa
NADES, Water remediation, Green Solvents
Environmental science and engineering
Contribute to the development of new methodologies for water treatments. Draft a publication with the latest findings.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Light-induced metal ion (de)complexation
Academic Program: Environmental Science and Engineering
Water contamination is one of the most critical environmental hazards. The pollutants in the water are responsible for diseases, affecting its quality and availability worldwide. In our lab, we aim to develop new methods for water treatment using liquid membranes and adsorbent materials, which will guarantee access to clean water and recover valuable raw materials. This project is focused on using light as a driving force for metal complexation and removal from water. The student will develop expertise in metal complexation, binding constants and water treatment. It is also expected to develop excellent skills in several analytical techniques.
BAS/1/1087-01-01
andreia.farinha@kaust.edu.sa
Trace metals, Light-induced metal complexation, water treatment
Environmental science and engineering
Contribute to developing new liquid membranes for removal and recovery of trace metals. Draft a publication with the latest findings.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Bioaccumulation of emerging contaminants in Red Sea coral reef organisms
Academic Program: Marine Science

Emerging contaminants (i.e., contaminants that have been recently in the ecosystem) are becoming a concern worldwide. Several studies have shown that contaminants can bioaccumulate in marine organisms, causing physiological and morphological impacts. Among these contaminants are some stimulants (caffeine) and medicines (e.g., diclofenac) that are commonly used by humans and end up frequently in the marine environment. Limited information is available in the Red Sea regarding the accumulation of emerging contaminants in coral reef organisms. Nevertheless, previous research in the region showed high concentrations of contaminants like caffeine and diclofenac in water samples collected near urbanized areas. This project aims to quantify the concentrations of caffeine and diclofenac in biological tissues of reef-associated organisms like corals and algae. This study will serve as the foundation to conduct ecotoxicological studies to investigate the response of those organisms to different concentrations of these emerging contaminants and guide environmental regulation. The student will participate in the processing of samples, quantification of contaminants, data analyses and writing. If time permits, one lab-based experiment will be conducted to assess the responses of coral larvae to observed concentration ranges of targeted contaminants.

 

BAS/1/1109-01-01
eva.aylagasmartinez@kaust.edu.sa
Bioaccumulation; Contaminants; Coral reefs; Environmental Impacts; Ecosystem Management
Ecotoxicology and Marine Biology

Draft of a publication based on the data collected.

Develop and conduct a lab-based ecotoxicological experiment

Marine Science
Biological and Environmental Sciences and Engineering
Red Sea Research Center
Joint models for longitudinal and survival data
Academic Program: Statistics
Joint models are essential is most biomedical applications due to their ability to model two data types simultaneously. In this project, the aim istodevelop vignettes for the implementation of joint models within theR-INLAframework. The prospective candidate would gain coding skills in R and potentially develop new statistical methodologies to handle real-world phenomena within the context of joint models.​​
BAS/1/1667-01-01
haavard.rue@kaust.edu.sa
Statistics
​Statistics
​Vignettes for the implementation of joint models within the R-INLA framework​
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Continuous-mode SandX Rector Automation
Academic Program: Environmental Science and Engineering
SandX is a nature-inspired super-water-repellent material comprised of common sand grains coated with paraffin wax in a 1:1000 mass ratio. When applied as a 1 cm-thick layer on the topsoil, SandX can dramatically reduce (~80%) the evaporative loss of water and increase biomass growth and crop yields by up to 70%. SandX’s efficacy to boost crop yields has been established via multi-year field trials on barley, wheat, tomato, green pepper, onions, and okra (Please refer to https://pubs.acs.org/doi/10.1021/acsagscitech.1c00148). After about 9 months of its application, SandX degrades into Sand and becomes a part of the soil. We have conducted soil microbiome analysis and found that it has no adverse impact on soil microbial diversity. Currently, we are automating our pilot-scale continuous reactor for manufacturing SandX, which we are seeking an intern for assistance. The ideal candidate will have hands-on experience in automation, reactor engineering, control systems, sensors, software, data logging, etc.
BAS/1/1070-01-01
lisa.okiexposito@kaust.edu.sa
reactor engineering, automation, control systems, sensors, wetting, SandX
Engineering
1. assessment of off-the-shelf technologies 2. installation 3. testing, iteration, optimization 4. data collection and analysis 5. characterize SandX via contact angle goniometry 6. summarize findings in a report
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Reactions of lung surfactants at the air–water interface
Academic Program: Environmental Science and Engineering
We are studying the behavior of bio-surfactants at the gas–water interface as a function of water pH and acidity and gas composition. The intern will work with Dr. Peng Zhang and Mr. Muzzamil Eatoo (Ph.D. student) build an experimental setup to probe the changes in surfactants behavior in real time.
BAS/1/1070-01-01
muzzamil.eatoo@kaust.edu.sa
air-water interface, lung surfactants, pollution
chemical engineering
1. literature review 2. building experimental set up 3. analytical techniques - ESIMS and others 4. data collection and analysis 5. scientific report
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Development of electro-optical characterization techniques for solar cells
Academic Program: Materials Science & Engineering
The Ultrafast Dynamics (UFD) Research Group of the KAUST Solar Center (KSC), part of the King Abdullah University of Science and Technology (KAUST), welcomes applications for an internship of up to 6 months. Our present research facilities include a number of spectroscopic and electro-optical characterization techniques to study photovoltaic devices based on organic and hybrid (perovskite) semiconductors. The Ultrafast Dynamics (UFD) Group is looking for an excellent intern interested in both, setup development and experimental studies. LabVIEW or programing experience is a big plus. Experience with CAD software, 3D printing and PCB design are a plus. The main task of the student will be to upgrade (hardware and software) the existing sensitive External Quantum Efficiency (sEQE) measurement setup used to investigate sub bandgap absorption features in photovoltaic devices (perovskite and organic based devices). The student will learn how to operate the current setup and develop a new measurement routine to comply with IEC measurement standards (60904-8 and 60904-8-1). The student will establish an internal standard operating procedure for the setup and train colleagues to use it. Additionally, the student will contribute to debugging and optimizing the measurement programs for various charge extraction techniques, including Time-Delay Collection Field (TDCF), photo-charge extraction by linearly increasing voltage (photo-CELIV), and Time-of-Flight (ToF). The student will also learn to fabricate and characterize organic solar cells using a number of electro-optical techniques. By the end of the internship, the student will be highly experienced with the fabrication and electro-optical characterization of solar cells.
BAS/1/1381-01-01
jose.jurado@kaust.edu.sa
Photovoltaics, organic semiconductors, perovskite semiconductors, charge transfer states, charge extraction, solar cells, organic photovoltaics, perovskite solar cells
Materials Science
i- Familiarization of the student with the sEQE technique and IEC standards (0.5 months). ii- Optimization and improvement of the sEQE setup (2.5 months). iii- Debugging various measurement programs (2 months). iv- Fabrication and characterization of solar cells (1 Month)
Materials Science & Engineering
Physical Sciences and Engineering
KAUST Solar Center
Development of deep work function hole transport layer for organic semiconductor diodes.
Academic Program: Applied Physics
The Ultrafast Dynamics (UFD) Research Group of the KAUST Solar Center (KSC), part of the King Abdullah University of Science and Technology (KAUST), welcomes applications for an internship of 6 months (further extension possible). Our present research focuses on spectroscopic and electronic characterization of photovoltaic devices that use organic and hybrid (perovskite) semiconductors with aim undersanding the photophysics governing those device efficiencies.. This specific project is related to device engineering and characterization based on organic semiconductors. The Ultrafast Dynamics Group is looking for an excellent intern interested in experimental studies, involving material science and engineering. More specifically, the student will have to develop hole transport layers (HTL) for organic diodes, enabling to inject holes at deep energy levels in the most recent generations of semiconductors used for organic solar cells. The larger frame of this study is to understand and reduce the energy losses observed in organic solar cells, ultimately leading to higher efficiencies. In details, this development involves the choice of materials, and the optimization of processing conditions. It also involves characterization of the developed films including electrical (current vs. voltage curves), spectroscopic (electroluminescence, photovoltaic quantum efficiency spectra) and energetic (photoemission spectroscopy in air) characterizations. As such, this project is a wonderful opportunity to get familiar with a range of experimental techniques, relevant for the semiconductor research and industry: how to use them, and how to cure and interpret the data.
BAS/1/1381-01-01
julien.gorenflot@kaust.edu.sa
Photovoltaics, organic semiconductors, energy losses, electroluminescence, OLED, Hole transport layers, transport layer/organic interface, hole injection, hole extraction, solar cells, organic photovoltaics.
Material science, electronics, experimental/applied physics.
i- Get familiar solar cells and OLED processing and characterization (1 month). ii- Test of different HTL material and optimization of processing condition (5 month). iii- Use of those new HTL to study energy losses in organic solar cells (6+ month).
Applied Physics
Physical Sciences and Engineering
KAUST Solar Center
Quantifying microbial diagenesis in shallow marine carbonate sediment using CT-scan
Academic Program: All Programs
Academic Program: Earth Science and Engineering
Shallow marine carbonate sediment can be significantly modified by microbial activity through encrustation, endolithic activity, or micritization on the seafloor. These alterations can be preserved in the carbonate rock records and can be used as paleo-environmental indicators and promote the development of microporosity. To assess the extent of microbial diagenesis, we buried consolidated sediment plugs in various lagoonal environments around the Arabian Peninsula (KSA, UAE, Oman) and recovered them after one year. This research project aims to compare the pre- and post-burial CT scan models of the plugs quantitatively to identify potential microbial alterations. SEM, BS-SEM, and thin section imaging are also envisaged. For this project, we are seeking for a motivated student or recent graduate with a strong background in geomodelling/geodata science and/or carbonate sedimentology.
URF/1/4097-01-01
thomas.teillet@kaust.edu.sa
CT-scan; Geomodel; Carbonate; Diagenesis; Microbe;
Geoscience, Geodata, Carbonate Sedimentology
Post-processing and analysis of differential 3D CT-scans models and petrographic images showing microbial-related diagenetic features
All Programs
All Divisions
Ali I. Al-Naimi Petroleum Engineering Research Center
Geochemical investigation and abrasion experiments to decipher the origin of lime mud
Academic Program: All Programs
The origin of carbonate mud in modern and ancient carbonate sedimentary systems is still today the subject of debate among carbonate sedimentologists. Although there is extensive research on modern Caribbean systems, little research has been conducted in the Arabian Gulf since the 1960s. The aim of this project is to decipher the source and the origin of carbonate mud collected along the Arabian Gulf coasts (KSA, UAE) by (1) investigating the geochemical fingerprints of carbonate components (i.e ooids, peloids, bivalves…) and by (2) experimenting in the laboratory the abrasion potential of these specific components to produce mud. Correlation and mixing models will be used to explain the mud as a mixture of end-member components. For this project, we are looking for a motivated student/recent graduate with a background in carbonate sedimentology, analytical geochemistry and ease in laboratory work.
URF/1/4097-01-01
thomas.teillet@kaust.edu.sa
geochemistry; carbonate; diagenesis; lab experiment
Carbonate sedimentology; geochemistry; diagenesis
Geochemical analytical models and inventory of geo-chemical signatures of carbonate components (XRD, ICP-OES, isotops) and results of abrasion experiment results.
All Programs
All Divisions
Impact of mangrove ecosystems on carbonate sediment: integration of carbonate chemistry and petrography.
Academic Program: All Programs
Sedimentologists have long observed that mangroves have the ability to retain sediment and increase sedimentation rate in comparison to vegetation-free intertidal zones like mudflat and tidal creeks. Recently, in the context of climate change and ocean acidification, mangrove areas have become of interest to researchers as they can act as a sink for carbon/carbonate (blue carbon). However, there are few multidisciplinary studies that directly correlate the quantitative carbonate budget in mangroves to the nature of carbonate sediments, early diagenetic features and microbial erosion. This project aims to fill this gap by investigating both the changes of pore water carbonate chemistry between mangrove and tidal creeks (Red Sea coast; KSA) as well as sedimentological and diagenetic changes in sediments (petrography). The focus will be on both the meter (i.e., forest, marsh, mudflat) and centimeter (i.e., sedimentary structures) scales. For this project, we are looking for a motivated student/recent graduate, with a strong background in carbonate sedimentology, carbonate chemistry, fieldwork (in-situ analysis pH, pore water sampling…), and laboratory work (ICP-MS).
URF/1/4097-01-01
thomas.teillet@kaust.edu.sa
Mangrove, carbonate, sedimentology, diagenesis, chemistry
Carbonate ; Sedimentology; Chemistry
Sediment (thin section, sieving) and pore water analysis (pH, TA, DIC, OM...). Heterogeneities maps and petrographic images.
All Programs
All Divisions
Unraveling the molecular basis of coral symbiosis and bleaching
Academic Program: BioScience
Coral-algae symbiosis is essential for the survival and growth of corals and for the functioning of coral reef ecosystems. However, this symbiosis can be disrupted by various environmental stressors, such as high temperature, pollution, or disease. When this happens, the corals lose their algae and turn white, a phenomenon known as coral bleaching. Bleached corals are more vulnerable to mortality and may not recover their symbionts if the stress persists. Understanding the interactions between coral hosts and their algal symbionts under different environmental conditions is crucial for developing conservation strategies. At KAUST, we study these interactions at different levels, from the molecular underpinnings to the ecological contexts. We seek motivated students who want to learn more about this symbiotic relationship. The students will be able to learn various techniques and skills related to coral biology, molecular biology, and bioinformatics.
BAS/1/1036-01-01
guoxin.cui@kaust.edu.sa
coral symbiosis, coral bleaching, stress response
Ecological Genomics
This interdisciplinary project comprises wet-lab work (80%) and soft skills (20%). Soft skills are not mandatory but can be acquired during the project through the intern's initiative. The project deliverables include: 1) Regular plan for growth and proliferation of sea anemones 2) Setting up experiments 3) Optimize existing protocols (nucleic acid extraction, HCR, FISH, etc.) 4) Basic data analysis (RNASeq, DNAseq,) 5) Consolidated report at the end of the internship The intern is expected to have experience in the following areas: 1) Basic wet laboratory skills 2) Animal care procedures 3) Molecular biology techniques such as RNA isolation and DNA isolation 4) Basic knowledge of UNIX and soft skills (considered advantageous) Overall, the intern should be proactive, eager to broaden their interests and meet with the principal investigator every two weeks to discuss research progress.
BioScience
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Red Sea Research Center
Coral reef ecology in a changing environment
Academic Program: Marine Science
Coral reefs are ecologically important ecosystems that are threatened by local human impacts, such as nutrient pollution and overfishing, and by global environmental change, such as ocean acidification and ocean warming. As a result, coral reefs have been in decline across the planet. In order to understand the future for coral reefs in the Red Sea, we must first understand the current status of reefs along the coast of Saudi Arabia, and then identify potential impacts of various environmental stressors. The goal of this project is to contribute to the broader research goals of the Global Change Ecology lab at KAUST, under the supervision of Professor Maggie Johnson. We are seeking students interested in studying coral reef community structure and function in present day conditions, monitoring how reefs in the Red Sea are changing over space and time, and contributing to field and lab experiments that aim to identify impacts of different environmental stressors on key reef species. This research presents the opportunity to develop projects tailored to the specific interests of students. Examples of possible projects include, but are not limited to, quantifying the cover and health of corals and algae on central Red Sea coral reefs, deploying instruments and analyzing data to evaluate variability in important environmental parameters (temperature, pH, dissolved oxygen), and conducting targeted field and lab experiments to identify effects of warming temperatures (or changing pH or dissolved oxygen) on calcifying algae and corals. This is an exciting opportunity to contribute to ongoing work and to develop new research projects in the area of human and environmental impacts on coral reefs. The Global Change Ecology lab is committed to building an inclusive community and research environment and encourages applicants from all walks of life.
BAS/1/1102-01-01
lucia.bravo@kaust.edu.sa
Red Sea, Coral Reef, Ecology, Marine Science, Global Change, Human Impacts, Coral, Seaweed, Ocean Acidification, Ocean Warming, Deoxygenation, Overfishing, Nutrient Pollution
Marine Science, Benthic Ecology, Coral Reef Ecology, Phycology
The student is expected to contribute to the research goals of the project, and to lead or participate as a co-author in a peer-reviewed scientific publication.
Marine Science
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Red Sea Research Center
Deep Learning for Visual Computing (Computer Vision, Machine Learning, Graphics)
Academic Program: Computer Science
The internship will be in the area of visual computing (computer vision, machine learning, computer graphics). The exact topic depends on the student's interest, student's background, and current research topics in the group. Many projects in the group deal with generative modeling and 3D reconstruction. For example, adversarial networks for synthesizing images, textures, point clouds, and 3D geometry; Generative diffusion models for synthesizing images, 3D, and animations; depth from a single image; image segmentation; primitive fitting; indoor room layout reconstruction; domain adaptation; few shot learning;​
BAS/1/1630-01-01
peter.wonka@kaust.edu.sa
Deep Learning; Machine Learning; Computer Vision; Computer Graphics; 3D Reconstruction; Generative Modeling;
Computer Vision, Machine Learning, Computer Graphics
There are no pre-determined deliverables. The goal is to learn more about the research process and to work towards a high-quality publication. Students will be involved in reading literature, discussing ideas, and implementation.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Coral trophic ecology in the Red Sea
Academic Program: Marine Science
Reef-building corals are mixotrophic organisms that can acquire autotrophic nutrition from their endosymbiotic algae and heterotrophic nutrition through predation on planktonic organisms. This trophic flexibility enables corals to thrive across a range of environmental conditions and may play a key role in their survival under climate change. However, our understanding of how different coral species modify their trophic strategy to survive stress or exploit variations in resource supply is limited. Determining the trophic plasticity of dominant coral taxa in the Red Sea is an important first step to understanding how these coral populations may change under future ocean conditions. We are seeking students interested in studying coral reef community structure and trophic ecology across the natural environmental gradients of the Red Sea. This research provides an opportunity to develop projects tailored to various interests of students. Examples of possible projects include, but are not limited to, quantifying the trophic ecology of hard and soft corals in the Red Sea, deploying instruments and analyzing oceanographic data to evaluate spatial and temporal variation in planktonic resource supply to coral communities, evaluating different biochemical techniques for quantifying coral nutrition (e.g., stable isotope analysis, fatty acid analyses, and metabolomics). This is an exciting opportunity to contribute to ongoing work and to develop new research projects around ocean-reef interactions in the Red Sea. KAUST is committed to building an inclusive community and research environment and encourages applicants from all walks of life.
BAS/1/1103-01-01
michael.fox@kaust.edu.sa
Red Sea, Coral Reef, Ecology, Oceanography, Primary Production, Stable Isotopes, Plankton, Chemistry, Upwelling, Coral Bleaching, Marine Heatwaves
Coral Reef Ecology, Oceanography, Marine Science, Biogeochemistry
The student is expected to contribute to the research goals of the project, and to lead or participate as a co-author in a peer-reviewed scientific publication.
Marine Science
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Red Sea Research Center
Developing a soft sensor to monitor water quality
Academic Program: Applied Mathematics and Computer Science
Ensuring the performance of wastewater treatment processes is important to guarantee that the final treated wastewater quality is safe for reuse. However, bacterial concentration present along the different stages of treatment process is not easily measured routinely for the plant operators. A moving horizon sensing approach based on neural networks has been proposed in [1] for measuring the bacteria concentration from easy to measure variables. The obtained model has been successfully tested on KAUST wastewater plant. In this project, the student will test and extend the model to other sets of data. A particular interest will be on incorporating self-calibration or self-training strategies to guarantee good performance of the proposed algorithm. [1] Mohammed Alharbi, Pei-Ying Hong, Taous-Meriem Laleg-Kirati. Sliding window neural network based sensing of bacteria in wastewater treatment plants, Journal of process control, Volume 110, February 2022, Pages 35-44
BAS/1/1627-01-01
peiying.hong@kaust.edu.sa
Neural network; wastewater treatment; microbial contaminants
Applied mathematics
We expect the student to test the existing model on a new dataset. Various neural network configurations will be also tested and compared.
Applied Mathematics and Computer Science
All Divisions
Graduate or Undergraduate
Safety-Guaranteed Planning and Control for Autonomous Underwater Robots
Academic Program: Electrical Engineering
With 2000km of coral reef and marine life surrounding it, the Red Sea is a rich and diverse ecosystem. Due to its ecological and economic importance, persistent monitoring of its key areas is critical for marine biology research, economic development, and environmental protection. However, long-term survey and observation of extensive marine environments are extremely labor-intensive and hence require new technological development. There have been a numerous efforts made in research and development of robots designed to autonomously survey and collect data in remote areas that are hard to access by human divers or require long-term and repeated monitoring. However, there remain technical challenges in implementing long-term autonomy in underwater robotics to allow a robot to navigate in close proximity of marine lives over an extended period of time while ensuring absolute safety and not interfering with their natural habitats. In this project, we explore learning-based planning/control approaches that combine optimization-based design and data-driven machine learning methods. Leveraging the optimal feedback control and reinforcement learning, we aim to develop a framework that enables the robot to 1) learn how the environmental characteristics (light condition, current speed, etc.) affect its navigation performance and 2) adaptively revise its control policies to safely and graciously navigate around marine habitats.
BAS/1/1695-01-01
shinkyu.park@kaust.edu.sa
Robotics, Feedback Control Systems, Optimization, Machine Learning
Robotics, Feedback Control Systems, Optimization, Machine Learning
We adopt computational methods from optimization and reinforcement learning to design libraries that allow robots of various types to safely navigate in underwater environments for automated data collection applications. The students are expected to collaborate with the lab members to explore creative ideas and implement them on the underwater robot being developed in the robotics lab at KAUST. To fulfill the requirements of the project, the students are expected to have experience working with robotic systems/software and confidence in Python programming. Software library design and experiment reports are expected to be delivered at the end of the internship program.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
How does Federated Learning work in the real world?
Academic Program: Computer Science
Federated learning (FL) is a novel paradigm enabling distributed machine learning (ML) model training, while ensuring that training data remains on individual clients. The increasing need for privacy makes FL a highly promising method spearheading the future of ML. Although theoretically elegant, FL faces significant hurdles when it comes to real-world implementation. The key obstacle towards wider proliferation of FL is an inherent heterogeneity of real-world settings. Clients that may participate in FL range from computationally limited Internet of Things (IoT) devices, over low-end smartphones, to high-end multicore GPU-powered smartphones and beyond. Furthermore, devices may host a range of different applications that are simultaneously competing for hosts’ limited resources. In this work we will for the first time quantify the effects of heterogeneity on FL performance in a real-world testbed. We will first conduct a survey of edge devices that are suitable for FL and assemble a representative, yet diverse, testbed at our KAUST premises. Building upon the existing open-source solutions, we will then program a FL training framework enabling rapid prototyping, experimentation, and measurements within our testbed. We will then conduct a set of experiments geared towards profiling the effect of device hardware, software, and usage heterogeneity of FL training accuracy, convergence time, fairness, and energy use. Finally, we will analyze the results and extract guidelines for efficient FL in heterogeneous environments.
URF/1/4699-01-01
marco@kaust.edu.sa
Federated Learning
Computer Science
- Physical testbed comprising a range of IoT and mobile devices. - Programming framework for FL experimentation and evaluation. - Executable scenarios encompassing different use-cases and levels of the underlying system heterogeneity. - Report on the experimental results with metrics designed to profile the effect of system heterogeneity on holistic performance of FL. - Guidelines for advancing FL in heterogeneous environments.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Dexterous Robot Manipulation using Physics Engine
Academic Program: Electrical Engineering
With the advance in sensing, actuation, and mechanical design, the robots are becoming more capable of performing complex and high-precision tasks ranging from autonomous driving in urban cities to handling packages in fulfillment centers. Control and planning of robot motion in real-world environments require high-fidelity models to simulate how the robot's motion impacts physical objects before they take action. Many well-established approaches in robotics rely on mathematical models due to their analytic tractability; however as robotic systems become complex and are required to interact in physical environments, we face limitations in finding analytically tractable models and begin to resort to new models that leverage the powerful computational resources embodied in robotic systems. To address the challenges in robot operation in physical environments, in this project, we explore the idea of building computational models based on physics engines and design feedback algorithms to autotune parameters of the model whenever the robot detects the discrepancy between the simulation and its experience in the physical world -- to reduce the sim-to-real gap. As key applications, we imagine to apply outcomes of the project to enable robotic manipulators to carry out the tasks in assembly lines and research laboratories that are labor-intensive and require dexterous skills.
BAS/1/1695-01-01
shinkyu.park@kaust.edu.sa
Robotics, Physics Simulation, Feedback System Design, Computer Science
Electrical and Computer Enginnering
This project aims to adopt a physics engine to build computational model and examine the fidelity of the model through experiments with manipulators in the robotics lab at KAUST. The students are expected to collaborate with the lab members to explore creative ideas and implement them on physical robotic systems. To fulfill the requirements of the project, the students are expected to have experience working with robotic systems/software and confidence in Python programming. The model design and experiment reports are expected to be delivered at the end of the internship program.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Siliciclastics in Al Wajh carbonate lagoon
Academic Program: Earth Science and Engineering
Afro-Arabian desert belt export at least half of the world's dust. Dust provides essential minerals for life to thrive on Earth, both on land and in the sea. Dust provides iron to the ocean, which is one of the main limiting nutrients for photosynthetic algae. However, not much is known about how much dust is necessary to fertilize the ocean. The aim of the project is to analyze dust particles within marine sediments from Al Wajh lagoon, NE Red Sea. A sedimentological, mineralogical, and spatial analysis will be performed.
BAS/1/1399-01-01
manueleduardofuentes@kaust.edu.sa
Siliciclastics; Dust; Carbonate Platforms
Geoscience
-SEM, XRD and grain size analysis of dust in marine sediments. -A scientific report showing the results and interpretation of the measurements performed.
Earth Science and Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Seeing the invisible – air flow around droplet upon impact
Academic Program: Mechanical Engineering
The Droplet Lab in KAUST (Saudi Arabia) is hosting one student for a 4-6 months for a fully-funded paid research internship under the VSRP program. The student will learn about using high-speed photography and Schlieren imaging technique to visualize air flow around droplets upon impact. The student will work closely with Prof. Dan Daniel who has hosted several undergraduate students in previous years; all of whom have published papers at top journals (including Nature Physics and Physical Review X) during their internships. Any bright, motivated student with a background in the physical sciences (Physics, Mechanical Engineering, Chemical Engineering, Nanotechnology) is welcome to send his/her CV to danield@kaust.edu.sa
BAS/1/1416-01-01
danield@kaust.edu.sa
Droplet Physics, Schlieren Imaging, Fluid Mechanics
Engineering (Mechanical/Chemical), Chemistry, Physics
Learning how to perform high-speed imaging. For highly motiated student, one publication in strong peer-reviewed journal is to be expected
Mechanical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Engineering an isothermal amplification strategy for ultrasensivite and quantitative detection of microRNA cancer biomarkers
Academic Program: BioEngineering
Academic Program: Materials Science & Engineering
MicroRNA (miRNA) are short (~22 nt) non-coding RNA that have emerged as highly promising diagnostic and prognostic biomarkers due to their gene regulatory functions and dysregulated expression profiles in many diseases including cancer. While promising, their absolute quantification is challenging due to their short lengths, low circulating concentrations, and high sequence homology among family members. The gold standard method for miRNA detection, based on reverse-transcription qPCR, involves complex multistep procedures and requires thermal cycling, trained personnel and bulky equipment. This project aims to develop a novel quantitative and isothermal strategy for ultrasensitive detection of miRNA at the point-of-care, i.e. in a simple, cost-effective and efficient manner from minute volumes of a patient's biological fluid sample. This project will include advancements in biomolecular engineering, biomaterials development, and biochemistry. Ultimately, this project will lead to the development of highly sensitive point-of-care biosensors for cancer diagnosis, prognosis or monitoring based on multiplex detection of microRNA cancer biomarkers from a minimally-invasive 'liquid biopsy' test.
BAS/1/1426-01-01
erol.hasan@kaust.edu.sa
microRNA, cancer, diagnostics, biosensing, biomarkers
Bioengineering and Material Science
The aim of the project will be to deliver the following main objectives: - Develop an isothermal amplification methodology and strategy for miRNA detection - Characterize the analytical performance of the developed assay (sensitivity, specificity, dynamic range, etc) - Optimize the analytical performance and validate the assay with synthetic and clinical samples - Submit a report on the preliminary results which will be used as a platform for a peer-reviewed research article
BioEngineering
Physical Sciences and Engineering
Graduate or Undergraduate
Unraveling the molecular basis of immune signaling
Academic Program: BioScience
All animals and plants form ‘holobionts’ with a myriad of microorganisms. Accordingly, all organisms have evolved sophisticated immune mechanisms to fight pathogenic microbes, while attracting and nurturing beneficial ones. We use biochemistry, biophysics and structural methods such as X-ray crystallography, small angle X-ray scattering, nuclear magnetic resonance, cryo-electron microscopy and AI-based predictions to reveal the 3D structure of protein complexes involved in controlling the immune system. The student will be embedded in a team of structural biologists and will work on protein production, biophysical and 3D structural analyses. In KAUST we have access to state-of-the art instruments, including last-generation TITAN KRIOS electron microscopes, and high-field NMR; X-ray analysis is performed in France. Prior wet-lab experience would be a plus.
BAS/1/1056-01-01
Stefan.Arold@kaust.edu.sa
3D protein structures; immune system; protein production; biophysical and 3D structural analyses; NMR; electron microscopy
Structural Biology
The student will be embedded in a team of structural biologists and will work on protein production, biophysical and 3D structural analyses.
BioScience
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Protein Synthetic Biology
Academic Program: BioScience
The StruBE synthetic biology team is harnessing the power of proteins to forge innovative tools for biotech and medical applications. We are collaborating with material scientists and chemists to embed our designer proteins in novel materials or bioelectronic circuits. Ongoing projects include the design of next-generation biosensors, smart drugs, targeted therapeutic protein degrading systems, and designer enzymes for industrial applications. This entails experimental and computational work as well as the development of robotic liquid handling workflows. On the experimental side, a prospective student will be involved with protein production from E. coli or mammalian cell culture and the biophysical characterization of protein-protein interactions or enzymatic (whole-pathway) reactions. Prior wet-lab experience is a plus. A computational project in support of protein design and lab automation would require prior knowledge in Python programming. Hence, the interdisciplinary projects can host students from various backgrounds, e.g. from biochemistry, (bio)engineering, protein structural biology, or computer science.
BAS/1/1056-01-01
Stefan.Arold@kaust.edu.sa
structural biology; synthetic biology; protein design; bioelectrics & biosensors; computational biology; Python
Synthetic Biology
*protein production from E. coli or mammalian cell culture and the biophysical characterization of protein-protein interactions or enzymatic (whole-pathway) reactions. *A computational project in support of protein design and lab automation would require prior knowledge in Python programming.
BioScience
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Contextualized Embeddings for Biomedical Data
Academic Program: BioScience
Contextualized embeddings have revolutionized the field of machine learning. First, as a means to encode text in natural language applications and later on as a representational mechanism for other modalities including image, longitudinal, and high-dimensional structured data. In recent years, embedding approaches have been proposed to address problems in biology and healthcare, however, there are many important questions that require further investigation. For instance, i) how to effectively integrate embeddings for discrete elements with continuous measurements, ii) how to integrate granular temporal or ordering information into embeddings, and iii) how to effectively create embeddings for multimodal data. Successful applicants will work toward developing a model prototype addressing one of the questions above using state-of-the-art representation learning approaches based on deep learning architectures.
BAS/1/1115-01-01
ricardo.henao@kaust.edu.sa
Machine Learning, Representation Learning, Deep Learning, Natural Language Processing
Machine Learning, Representation Learning
Deliverables include a literature review of the state of the art, the implementation of a model prototype and experiments comparing to existing approaches in the literature.
BioScience
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Computational Bioscience Research Center
Characterization of IL11-deficiency in humans
Academic Program: BioScience
The student will investigate patients with germline homozygous variants in the cyokine IL11. He/she will make use of patient-derived fibroblasts and serum to confirm the pathogenicity of identified mutations and examine its downstream effects on cellular activity. Additionally, an animal model may be envisaged in collaboration with European group and new clinical assays may required to better define syndrome. This falls under the Smart Health Initiative towards the understanding of monogenic diseases. This may become a part of a PhD project.
I don't know what this is ?
bruno.reversade@kaust.edu.sa
IL11, fibrosis, Mendelian diseases
human genetics
literature review and knowledge of IL11 pathway cell culture with patient and established cell lines signalling assays including Elisa, western blots, QPCR, immnuoflorescence, metabolomics biomarker analysis in serum and supernatants animal modeling clinical analysis and comparison clear communication at lab meetings and project report
BioScience
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Security analysis of Docker-based containerized environments
Academic Program: Computer Science
Operating System (OS) virtualization, also known as container-based virtualization, has gained momentum over the past few years thanks to its lightweight nature and support for agility. However, its compelling features come at the price of a reduced isolation level compared to the traditional host-based virtualization techniques, exposing workloads to various faults, such as container escape. Those faults might be manifested as host OS bugs, container runtime vulnerabilities, and/or poor container deployment choices and profile configuration. The latter aspect is particularly critical as deployment and security configuration choices often need to be relaxed to meet the operational requirements of running applications leading hence to a widened attack surface. For example, if a container configured to be run with full privilege (or even with an extended set of capabilities) gets compromised, the latter might take control both of the hosting machine and the co-residing containers. The objective of this project is to perform a security assessment of containerized environments in order to unveil potentially dangerous container deployment and configuration options. This would enable identifying critical containers to closely monitor their behavior and detect erroneous security states as they occur. For more concrete discussions, we consider Docker, which stands out as the most adopted container technology.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
Virtualization, container security, Docker, Linux privileges,
Security, OS cybersecurity, virtualization, Docker
The expected outcome of this project is twofold. First, the student should come up with several real-life scenarios showcasing how potentially dangerous Docker container configuration and deployment options might be exploited in case of container compromise. Second, the student will collaborate with the team members to write a paper summarizing the findings exemplified by the previously defined scenarios.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Monitoring containerized environments for security state error detection
Academic Program: Computer Science
Operating System (OS) virtualization, also known as container-based virtualization, has gained momentum over the past few years thanks to its lightweight nature and support for agility. However, its compelling features come at the price of a reduced isolation level compared to the traditional host-based virtualization techniques, exposing workloads to various threats, such as container escape. In those threats, compromised or rogue containers might exploit existing vulnerabilities or poor container deployment choices to successfully inject security state errors (e.g., breaking out of the namespace isolation mechanisms and running as a root at the host level). To effectively detect those security state errors, we would like to monitor containers at the system call level as the latter accurately maps processes to their activities. Hence, the objective of this project is firstly to study and compare existing monitoring tools (generic such as strace, or container-specific such as sysdig) and select the most suitable one according to a set of criteria (e.g., resource consumption, offered monitoring options). Secondly, the chosen monitoring tool will be instrumented for different scenarios (benign and anomalous settings) to generate relevant datasets capturing the behavior of containers with respect to a set of planned (malicious and benign) activities within a time window. The datasets will be subsequently vetted to extract critical system calls and execution paths that need to receive attention in the runtime detection process.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
Containers, Docker security, container monitoring
Operating Systems, Security, Containers
Put in place and document an efficient container monitoring mechanism that will be used subsequently in conjunction with an error detection artifact to uncover erroneous security states in Docker-based containerized environments. Using the established monitoring mechanism, the student will run a set of planned container activities and build datasets that will be used for system call and execution path analysis.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Trees, Algebras, and Differential Equations: Extending the B-series.jl package for numerical analysis of initial value problems
Academic Program: Applied Mathematics and Computer Science
The bseries.jl software package (https://ranocha.de/BSeries.jl/stable/) is designed to facilitate analysis of numerical methods for initial value problems, by utilizing the relationship between Taylor series, rooted trees, and Hopf algebras. It implements a range of graph-based algorithms that enable the study of errors in numerical methods, for instance revealing how the energy of the approximated system will evolve. It also allows for the design of novel methods. Its current functionality is primarily focused on Runge-Kutta methods.In this project we seek to extend the capabilities of bseries.jl to new classes of methods and/or new kinds of analysis. There are a number of possible directions and the specific one chosen will depend on the interests and knowledge of the student. Possibilities include extensions to:- Multi-derivative methods - Partitioned methods (e.g. for Hamiltonian systems) - General linear (multistep, multistage) methods - Exponential methods - Alternative bases for order conditions - Application of simplifying assumptions in method design - Generalized additive Runge-Kutta methods - Characterization of energy-preserving B-series - Extensions of B-series, such as aromatic B-series, exotic B-series, and S-series.Additional topics and references for some of these topics can be found at https://github.com/ranocha/BSeries.jl/issues/8.
BAS/1/1616-01-01
david.ketcheson@kaust.edu.sa
Applied Mathematics and Computer Science
Applied Maths and Computer Science
To be discussed in interview with applicant
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Extreme Computing Research Center
Seawater Reverse osmosis (SWRO) pretreatment impact on microbial growth potential
Academic Program: Environmental Science and Engineering
The desalination of seawater using reverse osmosis membranes (RO) is an attractive solution to global freshwater scarcity. However, membrane performance is reduced by (bio)fouling. Considering that (i) the global production of desalinated seawater by RO is at 65.5 million m3/d, (ii) a membrane single membrane production capacity of 12 m3/d, and (iii) an average membrane replacement rate of ~10% to 15% per year due to fouling, then about 825000 membranes go to waste every year worldwide. Among the fouling types, biofouling –membrane deposition of bacterial cells and subsequent microbial growth– is the most difficult to alleviate. The lifetime of RO membranes would be extended if seawater pretreatment units, prior to RO, efficiently removed the material causing biofouling. The problem with pretreatment in desalination plants is that we lack a robust biological-based method to assess their efficiency to remove biodegradable nutrients and microbial cells. Current methods to assess the quality of seawater entering the RO such as turbidity and silt density index do not inform the water’s microbial growth or biofouling potential; hence the performance of the receiving RO membrane is jeopardized.
FCC/1/1971-42-01
luca.fortunato@kaust.edu.sa
Desalination; biofilm; biofouling; Reverse Osmosis;
Environmental science and engineering
This research will develop a biological-based monitoring system based on microbial and biofilm growth potential to determine the efficiency of filtration pretreatment processes. The idea is to develop and implement a sensitive method to assess the microbial and biofilm growth potential SWRO pretreatment units.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
ClO2 for biofouling control in Seawater Reverse Osmosis
Academic Program: Environmental Science and Engineering
It is desirable to apply a disinfectant for biofouling control in the membrane elements as well as in the intake system of seawater reverse osmosis (SWRO) plants. Chlorine dioxide (ClO2) would be an ideal candidate, since it is a highly effective disinfectant, does not produce chlorinated disinfection by-products, and as a dissolved gas it easily passes the membrane, allowing disinfection of the permeate side. Compared to chlorine and chloramine, ClO2 is a less aggressive oxidant, however, it has a higher oxidizing capacity. Polyamide membranes are known to be easily damaged by oxidants, and the potential for membrane damage is hampering the application of ClO2 in reverse osmosis. The presence of bromine, relatively high pH and high temperature, suggests that in Middle East, it is a serious possibility that membrane damage occurs in SWRO. Conversely, it is highly likely that ClO2 dosing is effective against biofouling, since it is an effective disinfectant and it can easily be transported into the biofilm and through the membrane.
FCC/1/1971-42-01
luca.fortunato@kaust.edu.sa
Desalination; Chlorination; biofouling; disinfection;
Environmental science and engineering
The main objective is to evaluate the potential membrane damage due to ClO2, and the second objective is to evaluate the effect of ClO2 on fouling.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
Breaking the Vehicle Over-The-Air Update System
Academic Program: Computer Science
A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and vulnerabilities, which can lead to catastrophic failures that threatens human lives. This makes the manufacturers liable to such failures and thus often caused millions of vehicles recalls for repair. A smart solution is to take advantage of the vehicle connectivity to the Internet and surrounding and perform Over-The-Air (OTA) software and firmware when needed, very similar to smart phone software updates. It is clear that this process is critical and can have negative consequences if the OTA update system unreliable and insecure. We have introduced an OTA protocol and corresponding Proof of Concept (PoC) implementation that ensure an end-to-end chain of trust between all stakeholders: the manufacturer, suppliers, brokers, and the vehicle.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
The Update Framework, Chain-of-Trust, Security, Over the Air software/firmware updates.
Connected Vehicles, Autonomous Vehicles, Software updates, Over-the-Air (OTA), security
The goal of this project is to demonstrate some attacks by running the PoC on embedded devices or even in a real vehicle. The role of the intern will be to understand the system and extend the demos we have already done in software, and experiment them empirically on real relevant devices. The objectives are to (1) raise awareness to the consequences of not doing OTA updates right, (2) to gauge if our system is secure empirically (3), and to improve it if is not.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Vehicle Intrusion Resilience Systems in Action
Academic Program: Computer Science
A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and intrusions, which can lead to catastrophic failures that threatens human lives. A Fault and Intrusion Resilient System (FIRS) is a vehicle middleware that can mask the effect of a failure or intrusion. Contrary to Intrusion Detection and Protection Systems, FIRS ensures the continuation of the function despite intrusions. FIRS works as follows: it allows an application to run different replicas on different ECUs simultaneously. For each function executed by the application, an agreement is collected from a majority of ECUs through the (in-vehicle) network, and the corresponding output is returned. As long as the majority is not compromised, the integrity of the returned output is guaranteed despite the existence of faults or intrusions in the rest of ECUs. We have an implementation of a FIRS protocol that we are experimenting on Omnet++ simulator.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
Intrusion Resilience Systems (IRS) for modern vehicles, CAN Vehicular networks,
Intrusion Resilience, Intrusion detection and prevention, Vehicular networks, CAN, Byzantine Fault Tolerance
The goal of this project is to create a demo that validates the FIRS on a real hardware and software. The intern will build a small testbed of networked embedded devices, e.g., RaspberryPIs or ECUs. Two network types are of particular importance: (1) the widely used broadcast-based Control Area Network (CAN), can be built using RaspberryPIs and CAN transceivers; and (2) the more recent efficient Ethernet for Automotive that, as the name indicates, has similarities to the Ethernet protocols in IT networks. The objectives of the work are to understand how FIRS behaves empirically, build the small testbed for validation, and demonstrate the work in a sub-real environment.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Rejuvenation of Diverse FPGA Softcores in a SoC
Academic Program: Computer Science
A field-programmable gate array (FPGA) is an integrated circuit designed to be reconfigured by the user after manufacturing to build a System-on-Chip (SoC) embedded device. The needed logic is usually implemented as a software image and then instantiated on the FPGA to inherit the nice properties of hardware, like higher speed and better security. Unfortunately, since the image itself, e.g., a Softcore that represents a Processing Unit, is a software, it is prone to faults and vulnerabilities that manifest after instantiation on the FPGA. Unfortunately, an Advanced Persistent Threat (APT) is possible if a determined adversary managed to discover a new vulnerability to initiate a zero-day, leaving no chance for classical detection and prevention tools to recover. In addition, the softcore can include bugs and glitches that manifest only at run time. Fault and Intrusion Tolerance (FIT) is a technique used to make a process resilient to such attacks by masking them. A FIT protocol replicates the processors, i.e., a softcore in our case, by running three versions simultaneously, and collecting a majority agreement (or consensus) on each operation. If the majority (e.g., 2/3 processors) did not fail at the same instant, the fault is masked, and the SoC continues operation as designed. This requires some level of diversity in the running softcore to increase the chances of independence of failures.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
FPGA, Microblaze, RISC-V, Openpiton, Fault and Intrusion Tolerance
FPGA, System on Chip, Replication
The goal of this project is to experiment running an FIT we are implementing on a diverse softcores, e.g., Microblaze, RISC-V, Openpiton, etc., on an FPGA and simulate some fault or attacks. We are experimenting the concept on a Xilinx Zinc board using equivalent replicas. The objectives are to check the feasibility of running the FIT with different softcore types and evaluate the behavior in action. The intern will acquire all this knowledge and publish the results by working with a team of experts.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Useful Bitcoin Mining with a Matrix-based Puzzle
Academic Program: Computer Science
Cryptocurrency and blockchain technologies are increasingly gaining adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain’s security, asset generation, and maintenance. In most cryptocurrencies, and mainly Bitcoin, the “work” a miner must do is to solve a cryptographic puzzle: to find a random nonce that once (cryptographically) SHA-256 hashed with a perspective block header, returns a 32 Bytes number having a leading pre-defined number of zeros (called difficulty). This puzzle represents the PoW, and lives forever in the blockchain (together with the block), allowing for future verifications. The main property of this puzzle is being very hard to solve, but easy to verify. Unfortunately, solving the puzzle is a very controversial being computation-hungry process that manifests in very high energy consumption (e.g., similar to the total electricity consumption of Denmark in 2020). Although other environment-friendly solutions are being suggested, e.g., Proof of Stake, the Bitcoin community has no plans to change the mining method using cryptographic puzzle. Shutting done the Bitcoin network is not an option either because it can disrupt the global economy with a market cap of around half trillion dollars as per today. Proof of eXercise (PoX) is an alternative puzzle that is getting more acceptance in academia. PoX suggests a matrix-based puzzle, e.g., matrix product and determinant calculation, that has close security properties to the cryptographic puzzle, but has at the same time useful benefits for the community, e.g., DNA and RNA sequencing, protein structure analysis, im-age processing, data mining [16], computational geometry, surface matching, space model analysis, etc. While computing the matrix product is very hard (which is required by design), its verification is also hard, making it infeasible. PoX proposes a probabilistic verification protocol that challenges the miner to only give the results of selected columns x rows multiplication for verification, thus making verification easy. Since selection is random, the miner cannot guess the columns x rows apriori, and thus must have computed the matrix correctly.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
Bitcoin, Proof of eXercise, Bitcoin mining, Matrix product
Supercomputing, Blockchain, Cryptocurrency
The goal of the project is to experiment the feasibility of this matrix-based puzzle empirically on the Sheheen super computer at KAUST. The intern will work with RC3 experts and Shaheen engineers to realize the experiments. The objectives of the project are to present to the community evidences that the proposed PoX puzzle is a reasonable alternative to the cryptographic puzzle from the security and monetization perspectives (e.g., the miner has more incentives since it gets paid by the problem proponent as well and by getting the coins while mining). The results of the project will be published or commercialized.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Advanced Breach and Attack Simulation using ML
Academic Program: Computer Science
Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these tools can be using scripts of known attack vectors, and running them in sequence. This, however, does not cover unknown zero-day attacks. An intelligent way would be to try to account for potential attack that are unknown. We envision that using some Machine Learning techniques trained on some types of vulnerabilities can make this automation smarter.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
Cybersecurity, Machine Learning, Deep Learning, Breach and Attack Simulation,
Cybersecurity, Machine Learning, Deep Learning, Penetration testing
The goal of this project is to experiment the use of Deep Learning or Generative Adversarial Networks as a tool to optimize the Breach and Attack Simulation. The intern will make use off-the-shelf tools that follow the same method for the detection of critical faults, e.g., memory overflows, and extend it for more security vulnerabilities (e.g., network). The objectives of the project are to understand the feasibility of ML model in optimizing BAS tools and publish the results as a paper or commercialize the project.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Achieving sustainable urban greening through the integration of anaerobic membrane bioreactor and nature-based biofiltration landscaping gardens.
Academic Program: Environmental Science and Engineering
The anaerobic membrane bioreactor is able to clean municipal wastewater at potentially lower energy costs than the conventional aerobic treatment processes 6. Anaerobic microorganisms within the membrane bioreactor do not remove ammonia and phosphate, hence resulting in a final effluent quality that can serve as liquid fertilizers for the landscaping plants and trees. Nature-based biofiltration system is a sand filtration unit that will consist of specifically layered filter media with saturated zone at the bottom, and planted with local plants and trees that are effective in pollutant and nutrient removal. Additional physical removal of contaminants is further achieved as the water flow through the filter media. Clean treated wastewater is collected at the drainage layer and can be reused for other purposes. By integrating the anaerobic membrane bioreactor-based wastewater treatment plant with the nature-based biofiltration landscaping feature, we can achieve the following: - Near zero-energy cost in treating municipal wastewater - Landscaping features with minimal water loss - Recovery of clean water that can be reused for other purposes
BAS/1/1033-01-01
peiying.hong@kaust.edu.sa
Nature-based solutions, Biofiltration
Environmental science and engineering
- Operate a high-throughput column experiments to determine how different variables (SandX, biochar, type of wastewater, plants, emerging contaminants) can affect the nature-based biofiltration columns - Monitor water quality - Monitor microorganisms
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
The KSA Native Genome Project
Academic Program: Plant Science
Environmental protection via the conservation of biological diversity is a central theme of the Kingdom of Saudi Arabia’s (KSA) Vision 2030. Biologically characterizing ecosystems that are relevant to the mission of MEWA’s National Center for the Development of Vegetation Cover & Combating Desertification (NCDVC&CD) would provide essential data as to how and why a particular ecosystem operates in a particular environment. This information can then, in turn, be used to engineer large-scale ecosystems that are sustainable, will positively influence vegetation cover, and will fight against desertification across the KSA. In partnership with the NCDVC&CD, the CDA are studying many common and endangered plant species of cultural, religious, medicinal and pastoral significance. This project will establish a robust pipeline/infrastructure to characterize 100s-1000s of different ecosystems across the Kingdom, both terrestrial and aquatic, as part of a broader initiative entitled the KSA Native Genome Project (KSA-NGP) under development. Furthermore, it will produce a new cohort of scientists trained in the study of ecosystem biology and its application to environmental protection and conservation.
FCS/1/4516-01-01
nahed.mohammed@kaust.edu.sa
Plant Biology, Genomics, Environmental protection, Conservation, Biodiversity, Sustainable Agriculture, food security
Genomics, Plant biology, Bioinformatics, Molecular Biology
- Generate high-quality genomic resources for all native plants under study, and associated microbial species in the KSA - Investigate their ecosystems with respect to metabolomes, root development/architecture, and microbiomes
Plant Science
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Center for Desert Agriculture
Biodiversity of Red Sea Reef Fishes
Academic Program: Marine Science
Documenting reef fish communities through surveys and collections across spatial and temporal scales is key to understanding changes of biodiversity across natural environmental gradients and local habitats, or to predict future trajectories in response to local and global stressors. Using a range of sampling and survey methods, we are building a comprehensive picture of fish communities within the Red Sea. Work will focus on the documenting fish communities in reef and non-reef habitats (e.g., mangrove, seagrass, macroalgae) to understand the connection and importance of habitats to ecosystem function and services. This focuses on conspicuous and cryptic fishes with opportunities to conduct field (diving, fish surveys and collections) and lab work (dissections, genetic barcoding).
BAS/1/1010-01-01
​​Biodiversity, Ecology, Marine Science
​Depending on the results, the student researcher may also contribute to a scientific publication detailing the work and findings​.
Marine Science
Biological and Environmental Sciences and Engineering
Red Sea Research Center
Connectivity of Shark Populations
Academic Program: Marine Science
​Ongoing research conducted by the Reef Ecology Lab and its collaborators have generated a diverse collection of shark and ray genetic samples. Analysis of these samples could provide insight into both elasmobranch biogeography and population ecology. Specific projects include investigating broad population genetics of hammerhead sharks in the Red Sea and Arabian Gulf or looking at fine-scale differences between reef shark sub-populations within the Red Sea. Undergraduate research will mostly focus on lab work, performing DNA extractions, running PCRs, and analyzing our existing collection of genetic samples. There may also be opportunities for fieldwork, including fish market surveys and catch-and-release sampling trips.​
BAS/1/1010-01-01
​Population Genetics
​Depending on the results, the student researcher may also contribute to a scientific publication detailing the work and findings. 
Marine Science
Biological and Environmental Sciences and Engineering
Red Sea Research Center
Integrating microbial electrolysis cell with anaerobic digestion to enhance resource recovery from waste activated sludge
Academic Program: Environmental Science and Engineering
Anaerobic digestion (AD) is a well-established biotechnology for generating methane from high-strength organic wastes such as waste activated sludge (WAS). However, several bottlenecks exist that hinder their widespread application such as low start-up time, low methane content and yield, and high susceptibility to environmental perturbation. One strategy to alleviate these bottlenecks is to integrate AD with microbial electrolysis cell (MEC). In MEC, acetoclastic electroactive bacteria (EAB) are considered the key functional groups responsible for recovering energy from acetate. In this project, we will study the effect of different start-up strategies with functionally redundant and efficient acetoclastic EAB on the performance of integrated MEC-AD fed with WAS.
BAS/1/1021-01-01
hari.anandarao@kaust.edu.sa
microbial electrolysis cell; anaerobic digestion; waste activated sludge
environmental science and engineering
Develop start-up strategies to enhance performance of MEC-AD
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
Tackling the challenges of NO-Laser Induced Fluorescence technique in hydrogen detonation
Academic Program: Mechanical Engineering
Compared to classical constant volume or constant pressure thermodynamic cycles, the detonation regime of combustion could increase by 40% the efficiency of engines. In line with the Paris agreement, identifying more efficient combustion processes is one of the strategies to limit CO2 emissions that contribute to climate change. For transportation, researchers focus on obtaining and controlling a self-sustained detonation in a specific engine (PDE or RDE). While the measurement of temperature and chemical species is of current practice in conventional combustion process (flames, engines, etc…), the experimental characterization of detonation relies on the determination of the detonation velocity, global pressure, and density gradient structure. These information are limited to validate numerical simulations and to be confident in the phenomenological comprehension extracted from it. While planar laser-induced fluorescence of hydroxyl radical (NO-PLIF) is a powerful technique to characterize reaction fronts, previous studies have shown significant limitations of this technique for detonation visualization. Not only restricted to reaction front visualization, this technique is also of interest as it can give access to 2-D temperature measurements in detonations. Objectives: The main objective of the project is to overcome the current limitations of the NO-PLIF imaging of detonation. This numerical investigation is based on a preexisting PLIF model that will be used (i) to identify the sensitive parameters (excitation line, laser energy, gas composition, etc…) of the PLIF intensity and (ii) to recommend experimental conditions to maximize the overall image quality.
BAS/1/1396-01-01
mhedine.alicherif@kaust.edu.sa
Detonation front; Laser diagnostics; Numerical simulations; Spectroscopic analysis
Combustion; shock waves
First, the student will have to become familiar with the principle of the NO-PLIF technique and the particularities associated with its usage on detonations, which has high pressure and temperature variations, high-speed flow (up to 2000m/s), etc… Second, the sensitivity analysis of the fluorescence signal will be conducted to identify the most sensitive parameters of the PLIF signal. Third, optimal operating conditions (≠ maximizing the fluorescence signal) will be identified and tested experimentally. Due to both the strong non-linearities between the PLIF signal intensity and each parameter involved, machine-learning approaches may be used to facilitate the identification of the optimal operating conditions.
Mechanical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Clean Combustion Research Center
Scaling Graph Neural Networks to 1000s of GPUs
Academic Program: Computer Science
Graph Neural Networks (GNNs) are a special type of deep neural networks that deal with graphs, instead of the more traditional images. GNNs are used in a variety of applications, from recommendation systems, to social networks, to computer security, to biological networks. The common characteristic is that graphs tend to be large and complex; therefore both training and inference require significant processing power. The goal of this project is to scale GNN training to thousands of GPUs. We will target our new supercomputer, Shaheen III, which is projected to include 2800 Nvidia Hopper super-chips than combine a CPU with a H100 GPU https://www.nextplatform.com/2022/09/26/kaust-hpe-shaheen-iii-supercomputer We will use the latest frameworks, such as Microsoft DeepSpeed, and we will target very large graphs.
BAS/1/1603-01-01
panos.kalnis@kaust.edu.sa
Deep Neural Networks; DNN; Graph Neural Networks; GNN; High Performace Computing; HPC
Machine Learning
- Tensorflow or PyTorch - based implementation - Project report
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Extreme Computing Research Center
Statistical and machine learning methods for health and environmental applications.
Academic Program: Statistics
The student will work on the development of statistical and machine learning methods for health and environmental applications. The topic is flexible and potential research areas include disease mapping, early detection of disease outbreaks, air pollution modeling, forest fires prediction, integration of misaligned spatial and spatio-temporal data, and the development of R packages for data analysis and visualization. Examples of research projects can be found at https://www.paulamoraga.com/research
BAS/1/1693-01-01
paula.moraga@kaust.edu.sa
statistics, mathematics, computer science
statistics, mathematics, computer science
The student will work on the development of statistical and machine learning methods for health and environmental applications. The topic is flexible and potential research areas include disease mapping, early detection of disease outbreaks, air pollution modeling, forest fires prediction, integration of misaligned spatial and spatio-temporal data, and the development of R packages for data analysis and visualization. Examples of research projects can be found at https://www.paulamoraga.com/research
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Egocentric Video Understanding
Academic Program: Computer Science
Have you ever imagined having a robot that cooks every meal for you? Have you ever dreamt about experiencing a different world in the Metaverse? Have you ever expected to have your glasses tell you where you left your keys and how to navigate to your favorite restaurant step by step? If yes, egocentric video understanding is what can lead you there. Egocentric videos are videos recorded from the first-person point of view, with the camera mounted on the head (e.g., GoPro) or smart glasses worn alongside the eyes (e.g., Google glasses), and they are what you actually see in your eyes. We need the AI system to automatically analyze and understand this type of videos to achieve the goals mentioned above. There are two key aspects in this problem: 1) large egocentric video data to fuel AI solutions; 2) effective techniques to generate correct predictions. For the first aspect, our IVUL group has devoted two years’ effort, together with 12 other universities as well as Meta (formerly Facebook), to achieve the largest egocentric video dataset called Ego4D. It contains 3000+ hours of egocentric video, spanning hundreds of scenarios captured by nearly 1000 unique camera wearers. Ego4D also defines various research tasks for egocentric video understanding, ranging from querying past memory, interpreting current behaviors, and forecasting future tendencies. For example, given a sentence “where and when did I last see my keys?”, the AI system returns the most recent video clip showing where your keys are. Or, the AI system automatically summarizes the video by telling you who is talking and what is his/her main point. Or the AI system predicts where you are walking to and what you are doing in the following minutes or even hours. For the second aspect, though Ego4D contains baseline solutions to each task, these solutions are far from practical for real-world application. There are two main challenges here. First, current solutions adopt techniques from video understanding tasks for third-person videos (where activities are recorded from a “spectator” view), which are dramatically different from egocentric videos in terms of recording perspective, camera motion, video continuity, etc. As a consequence, representations learned from third-person videos are not optimal to represent egocentric videos. We need to investigate novel feature representations specific to egocentric videos, or explore ways to smartly transfer the knowledge from third-person videos to egocentric videos. Second, egocentric videos pose new challenges for conventional methods due to their characteristics, such as noisy head motion, long videos and fragment actions. We need to address these challenges and improve the performance with novel techniques. In a nutshell, Ego4D is putting an apron on the robot and knocking on the door of the Metaverse, while at the same time, it is unveiling fresh challenges, which AI researchers are the key. It’s time to hop on board and contribute to this grand effort!
BAS/1/1653-01-01
bernard.ghanem@kaust.edu.sa
egocentric video; video understanding; computer vision;
Computer Vision; Machine Learning
(i) Effective feature representations of egocentric videos that benefit downstream tasks of egocentric videos, such as episodic memory, future anticipation; (ii) Novel techniques to transfer/translate between egocentric videos and exocentric videos; (iii) Improvement to retrieve ‘moments’ from past videos using a category, sentence or an object; (iv) Improvement to identify speaking faces in an egocentric video and summarize the speech.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Visual Computing Center
Next Generation Continual Learning
Academic Program: Computer Science
One of the most impressive abilities of human beings is the incremental learning ability. Since birth, we are always learning continuously without forgetting previously acquired knowledge that is useful for us. Could you imagine the amount of real-world applications that could be developed if we extended this human ability to modern-day AI systems (especially deep neural networks)? Modern deep neural networks adapt their parameters based on large-scale datasets to achieve state-of-the-art performance on specific computer vision tasks. However, due to legal and technical constraints and huge label diversity, deep learning models in real-world scenarios would rarely be trained just once time. Instead, they could be trained sequentially in several disjoint computer vision tasks without considering the data from previous tasks (because it may no longer be available for example). Therefore, these networks should learn incrementally without forgetting the previously learned knowledge. This is known as Continual Learning (CL). Currently, there are two families of CL methods. The first is rehearsal or memory-based methods, which select and store the most relevant samples to remember the current task when the following tasks are learned. The second group involves regularized methods that penalize changes to the most relevant parameters for the previous tasks. While the main studied challenge in the literature is learning with the least amount of forgetting on previous tasks, there are several other unexplored factors that affect learning from a stream of data. For instance, How fast is the learner in adapting the parameters of the model when receiving a new batch of data? If the learner is too slow (expensive training routine), then samples from the stream could be missed and not trained on. Thus, how can we benchmark different continual learning methods under budget constraint training? Furthermore, most continual learning benchmarks are focusing solely on the image domain leaving the more challenging video data unstudied. In the video domain, one of the main issues has been the lack of realistic, challenging, and standardized evaluation setups, making direct comparisons hard to establish. Therefore, our group IVUL has developed vCLIMB, a novel video class incremental learning benchmark, to promote and facilitate research on continual learning in the video domain. Video CL comes with unique challenges. (1) Memory-based methods developed in the image domain are not scalable to store full-resolution videos, so novel methods are needed to select representative frames to store in memory. (2) Untrimmed videos have background frames that contain less helpful information, thus making the selection process more challenging. (3) The temporal information is unique to video data, and both memory-based and regularization-based methods need to mitigate forgetting while also integrating key information from this temporal dimension.
BAS/1/1653-01-01
bernard.ghanem@kaust.edu.sa
continual learning; online learning
machine learning; computer vision
(i) A novel memory sampling strategy that learns to select a different number of relevant frames per video to reduce memory consumption while the performance remains almost the same; (ii) Novel training techniques/schemes to reduce forgetting. (iii) Benchmarking different continual learning methods on more practical metrics.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Visual Computing Center
Graph Neural Networks for Science and Engineering
Academic Program: Computer Science
Lots of data in scientific and engineering applications come with natural graph structures such as molecules in Chemistry, proteins in Biology, particles in Physics, planets in Astronomy, Bulk and MOF materials in Material Science, social and citation networks in Data Science, point clouds and meshes in Computer Vision and Graphics and so on. To model the information of such objects with complex structures, Graph Machine Learning, especially Graph Neural Networks (GNNs), has been proven as one of the most promising tools. Graph neural networks are deep learning architectures that can be trained to represent graphs with node features and edge features. For example, a molecule can be represented by a graph, where each atom is a node in the graph, and each bond is an edge. The atom numbers and types of chemical bonds are the associated node and edge features respectively. A GNN model can be trained to predict the quantum properties by learning on density functional theory (DFT) datasets which has huge potential to advance scientific discovery. Our group at IVUL have developed methods for graphs in 3D vision, videos, data mining, and fundamental science. We have developed GNNs with more than 100 layers with DeepGCNs (ICCV’2019, TPAMI’2021), and PU-GCN (CVPR’2021) for 3D point clouds segmentation and generation, G-TAD (CVPR'2020), VLG-Net (ICCVW’2021), MAAS (ICCV’2021) and VSGN (ICCV’2021) for large-scale video understanding, and DeeperGCN (arXiv’2020), 1000-layer GNN (ICML’2021) and FLAG (CVPR'2022) for node, link and graph level property prediction on Open Graph Benchmark (OGB) datasets which have graphs span nature, society and information domains. Are you excited about working on complex graph-structured data to make advances in biology, chemistry, physics, computer science, and so on? Would you like to use artificial intelligence to make fast predictions about the 3D structure of molecules, thereby speeding up the drug discovery process? Are you motivated by applications to precision medicine, and would like to create AI that learns to recommend what specific drug is suitable for a particular patient? Or perhaps you are more interested in higher-level abstractions, and would like to build an AI-based partial differential equation solver. All of these complex problems can be modeled through graph-structured data, and research in Graph Neural Networks can bring us closer to solving them. GNN has untapped potential in tackling graph based problems in Science and Engineering. However, more work is needed to explore the unique challenges to each scientific domain. In this project, you will have the chance to learn how to build large-scale graph neural networks and apply them to scientific and engineering applications.
BAS/1/1653-01-01
bernard.ghanem@kaust.edu.sa
graph neural networks
machine learning, AI for science
(i) Identifying the ground challenges of graph based problems in Science and Engineering; (ii) Collecting or processing the desired data into graph formats; (iii) Proposing novel GNN architectures and training techniques to tackle the challenges of learning on these graph data; (iv) Training and evaluating the proposed methods on specific metrics; (v) Producing well-performing and reproducible results and releasing the modular and reusable codebase to the research community.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Visual Computing Center
Next Generation 3D Understanding
Academic Program: All Programs
Academic Program: Computer Science
We are living in a 3D world. A broad range of critical applications such as autonomous driving, augmented reality, robotics, medical imaging, and drug discover rely on accurate representation of the three-dimensional data. While enormous efforts have been devoted to images and languages processing, it is still at an early stage to apply deep learning to 3D, despite their great research values. Here, we launch the project of next generation 3D understanding, and want to appeal more young talents like you to make this happen. We are particularly interested in two topics of the next generation 3D understanding: 1) a large-scale pretrained foundation 3D understanding model; 2) a vision generalist model that connects 2D and 3D vision data such as images, point clouds, and RGB-D. For the first topic, you might have heard that the trillion-parameter AI language model Switch Transformer by Google Brain excels across nature language processing tasks, and you might also know about the recent model Imagen with over 2 billion parameters can produce Photorealistic images from texts. Both of them are great examples of the power of large-scale models. Unfortunately, in 3D understanding, even the largest well-known network is still with less than 100 million parameters. How to increase the scale of 3D models in order to further unveil the power of deep learning in 3D application is a promising research direction. For the second topic, as a human, we can understand vision data despite its modality (2D or 3D). It is a step towards general artificial intelligence in computer vision to propose a single model that is able to have all knowledge about vision including 2D (images, videos), 3D (point clouds, RGB-D). This is an interesting topic but is under explored in the community. Our group at IVUL has put tremendous efforts and gained significant achievements in both topics. For the large-scale pretrained foundation model, our group is the first in the world that successfully trained a model with over 100 layers that achieved state-of-the-art performance in 2019 (DeepGCNs-ICCV19’). We broke our own record to 1000 layers in 2021 (GNN1000-ICML21’). Recently, we also propose scalable 3D networks with high inference speed in 2020s (ASSANet-NeurIPS21’, PointNeXt-arXiv22’). For the vision generalist model, our group has published impactful papers that involve understanding both view-images and point clouds (MVTN-ICCV21’, VointCloud-arXiv21’). Moreover, we have multiple ongoing projects in both directions. If you want to become a part in next generation 3D understanding, do not hesitate to join this project and achieve more with us!
BAS/1/1653-01-01
bernard.ghanem@kaust.edu.sa
3D computer vision
3D computer vision
(i) proposing a new large-scale foundation model for 3D understanding; (ii) proposing self supervision techniques for training large-scale 3D models with limited data; (iii) proposing novel generalist vision models that are able to tackle both 2D and 3D understanding; (iv) proposing novel techniques for training this cross-modality generalist vision model.
All Programs
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Visual Computing Center
Enzymatic Synthesis – Enzyme Characterization and Cascade Engineering
Academic Program: BioScience
Biocatalysis has found numerous applications in various fields as a green and sustainable alternative to chemical catalysis. The potential of using enzymes in organic synthesis is high, especially to make chiral compounds for pharmaceuticals. The project focuses on the expression, purification and characterization of various enzyme classes for different synthesis targets. The enzymes will be tested alone or in combination in aqueous catalysis reactions and the influence of composition on selectivity and reactivity will be studied. Project-duration will be 3-6 month, details of arrival/departure dates to be discussed.
BAS/1/1385-01-01
dominik.renn@kaust.edu.sa
Biocatalysis, Enzymes, Cascade Engineering, Enzyme Characterisation
Biochemistry
Students shall extend their general knowledge and skills in molecular biology and protein biochemistry. An emphasis will be put on expression, purification and characterization techniques. Students will be taught to work independently on projects, yet strengthening their critical sense to develop new ideas. In the course of the internship students shall demonstrate this understanding during oral presentations and one final written report.
BioScience
All Divisions
Graduate or Undergraduate
KAUST Catalysis Center
Biocatalytic Amine Synthesis
Academic Program: BioScience
Enantiomerically pure chiral amines are valuable building blocks for the synthesis of various compounds such as pharmaceutical drugs or agrochemicals. Biocatalytic amine synthesis allows a cost-effective and sustainable preparation of chiral amines in enantiomerically pure form. The project aims for the identification, engineering and application of new amine forming enzymes to biocatalytic retrosynthesis and new enzyme cascades. Project-duration will be 3-6 month, details of arrival/departure dates to be discussed.
BAS/1/1385-01-01
dominik.renn@kaust.edu.sa
Biocatalysis, Enzymes, Cascade Engineering, Amines
Biochemistry
Students shall extend their general knowledge and skills in molecular biology and protein biochemistry. An emphasis will be put on expression, purification and characterization techniques. Students will be taught to work independently on projects, yet strengthening their critical sense to develop new ideas. In the course of the internship students shall demonstrate this understanding during oral presentations and one final written report.
BioScience
All Divisions
Graduate or Undergraduate
KAUST Catalysis Center
The Development of Organic Transformations via Photocatalysis
Academic Program: BioScience
Academic Program: Chemistry
Over the past few years, the field of photocatalysis has demonstrated its potential to drive complicated chemical reactions under mild conditions using visible-light as an energy source and inexpensive, bench-stable substrates as feedstocks. Our group has been focused on the development of photocatalyzed organic transformations via diverse pathways including single electron transfer (SET), energy transfer (ET), photo-excited metal cross-coupling, electron-donor-acceptor (EDA) complex. Project-duration will be 3-6 month, details of arrival/departure dates to be discussed.
BAS/1/1385-01-01
chen.zhu@kaust.edu.sa
Photocatalysis
Chemistry
Students shall extend their general knowledge and skills in chemistry and photocatalysis. An emphasis will be put on characterisation techniques. Students will be taught to work independently on projects, yet strengthening their critical sense to develop new ideas. In the course of the internship students shall demonstrate this understanding during oral presentations and one final written report.
BioScience
All Divisions
Graduate or Undergraduate
KAUST Catalysis Center
Electro-catalyzed C-C and C-X bond cross-couplings
Academic Program: Chemistry
The development of sustainable and scalable catalytic methodologies to access structurally diverse organic compounds is a long-term aim for organic chemists. In this regard, organic electrocatalysis has been developed as an attractive catalytic platform making use of renewable electricity instead of stoichiometric oxidants or reductants. We are focusing on the construction of new C-C and C-X bonds and their mechanistic studies under electrochemical conditions with or without the aid of metal catalyst. Project-duration will be 3-6 month, details of arrival/departure dates to be discussed.
BAS/1/1385-01-01
chen.zhu@kaust.edu.sa
Electro-catalyzed, C-C bond formation, C-X bond cross-couplings, Electrocatalysis
Chemistry, Electrocatalysis
​Students shall extend their general knowledge and skills in chemistry, and electrocatalysis. An emphasis will be put on electrocatalysis steps and techniques. Students will be taught to work independently on projects, yet strengthening their critical sense to develop new ideas. In the course of the internship students shall demonstrate this understanding during oral presentations and one final written report.
Chemistry
Physical Sciences and Engineering
Undergraduate
KAUST Catalysis Center
Functional metagenomics: AI-based analysis of complex microbial interactions
Academic Program: BioEngineering
The amount of available protein sequence data is rapidly increasing, for example through applications of sequencing technologies to metagenomics. To understand biological phenomena on a molecular scale, it is crucial to determine the functions of proteins as well as their interactions. Experimental identification of protein functions will not scale to the current and rapidly increasing amount of available protein sequences. Function prediction methods using machine learning may be used to determine protein functions from their sequence. However, proteins rarely function alone but rely on other proteins to perform their function through direct and indirect interactions. The aim of the project is to computationally characterize the functions and interactions of proteins in metagenomes through the development and application of novel AI methods.
BAS/1/1659-01-01
robert.hoehndorf@kaust.edu.sa
metagenomics, Artificial Intelligence, machine learning, protein function prediction, microbiome, genomics
Bioinformatics
Month 1: identification of AI methods, characterization of metagenomics dataset, technical presentation Month 2: preparation and preprocessing of metagenomics data (QC, assembly) Month 3: implementation of AI method and data analysis, evaluation Month 4: combination of AI methods: protein functions and interactions between proteins Month 5: evaluation results, quantitative characterization Month 6: write-up
BioEngineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Computational Bioscience Research Center
Neuro-symbolic AI algorithms
Academic Program: Computer Science
Symbolic, logic-based languages are inherently interpretable by humans. Symbols are entities standing for other entities and can be combined to form more complex expressions. Symbol systems are therefore well suited to explain and answer questions of “how” and “why” an intelligent agent (human or artificial) arrived at a decision. Knowledge-based systems based on logic have traditionally been used successfully in question answering (formulated as computing entailments, i.e., statements that must be true if all the axioms are assumed to be true) and can generate novel and “surprising” answers through deductive inference. However, they are not well suited to dealing with incomplete or noisy information or identifying patterns from unstructured data. Machine learning methods, in particular neural networks, can deal with noisy and incomplete data substantially better than symbolic, logic-based methods. However, they operate mainly as black boxes which do not make the logic underlying a decision making process available. Neuro-symbolic methods in Artificial Intelligence aim to combine logic-based AI methods and neural methods to overcome the limitations of both. The aim of the project is the identify, implement, evaluate, and improve neuro-symbolic methods. The baselines and experiments will focus on one of two possible areas of application: biomedical data where a large number of knowledge bases has been developed, or common sense knowledge.
BAS/1/1659-01-01
robert.hoehndorf@kaust.edu.sa
neuro-symbolic, Artificial Intelligence, machine learning, Semantic Web, logic, bioinformatics, common sense reasoning, automated reasoning, knowledge graph, knowledge representation
Artificial Intelligence
Month 1: identification of algorithm, technical presentation Month 2: implementation, baseline experiments Month 3: algorithm evaluation Month 4: analysis, improvement and tuning Month 5: experimental results, theoretical results Month 6: write-up
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Computational Bioscience Research Center
Iproved oil recvery from carbonates
Academic Program: Energy Resources and Petroleum Engineering
Perform flow experiments on limestone cores. Propare and age cores. Understand principles of fluid flow in proous media and some of he involved equipment
BAS/1/1780-01-01
ksenia.kaprielova@kaust.edu.sa
petroleum, IOR, carbonate, microporous, EOR, chemical flooding
Petroleum or chemical engineering
Report with experimntl results
Energy Resources and Petroleum Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Nanovisualization
Academic Program: Computer Science

During the research internship students will learn about the nanovisualization technology which combines computer graphics and visualization for nano-structures in life sciences and biotechnology. Nanovisualization poses several new technological challenges that are not reflected in the state of the art computer graphics and 3D visualization as of today. The underlying domain requires new techniques for multi-scale, multi-instance, dense, three-dimensional models which we never subject of technological advances in 3D graphics before. These scenes are of gigantic sizes and unmatched complexity. Therefore the task in nanovisualization is to thoroughly revisit all technological aspects of rendering, visualization, navigation, user interaction, and modeling in order to offer algorithmic solutions that address new requirements associated with the nano and microscopic scales.Throughout their stay, students will be working in team with researchers on specific assignment for a particular scientific work or solving a technical challenge in the field of computer graphics and visualization. Nanovisualization is one of the key components in creating, studying, and understanding scale-wise small (but complex) systems. As such it will become a key technology in the upcoming industrial revolution that will be heavily associated with the nano scale.The benefit for the students is to get familiar with nanovisualization research field, which is worldwide uniquely offered at KAUST. They will be integrated in working on a very important problems so far untouched in graphics and visualization that are very relevant for many societal challenges from the health, food, and energy sectors.

ASP/1/1669-01-01
ivan.viola@kaust.edu.sa
Nanovisualization
​computer science, applied mathematics, physics
​Students should work on an internship project and should implement a clearly specified task. It is expected that students are familiar with computer graphics and visualization and can document advanced skills in graphics programming​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Visual Computing Center
Formation of hydrogen peroxide in water microdroplet
Academic Program: Environmental Science and Engineering
Recent reports on the formation of hydrogen peroxide (H2O2) in water microdroplets produced via pneumatic spraying [1] or capillary condensation [2] have garnered significant attention. How covalent bonds in water could break under such mild conditions challenges our textbook understanding of physical chemistry and the water substance. While there is no definitive answer, it has been speculated that ultrahigh electric fields at the air-water interface are responsible for this chemical transformation. We are carrying out a comprehensive experimental investigation of H2O2 formation in (i) water microdroplets sprayed over a range of liquid flow-rates, the (shearing) air flow rates, and the air composition (ii) water microdroplets condensed on hydrophobic substrates formed via hot water or humidifier under controlled air composition. Our experimental results have challenged the putative claims of spontaneous H2O2 generation on the water surface [3, 4]. Additionally, new scientific questions along this theme have emerged that the VSRP intern will contribute to. References: 1. Lee, J. K.; Walker, K. L.; Han, H. S.; Kang, J.; Prinz, F. B.; Waymouth, R. M.; Nam, H. G.; Zare, R. N., Spontaneous generation of hydrogen peroxide from aqueous microdroplets. P Natl Acad Sci USA 2019, 116 (39), 19294-19298. 2. Lee, J. K.; Han, H. S.; Chaikasetsin, S.; Marron, D. P.; Waymouth, R. M.; Prinz, F. B.; Zare, R. N., Condensing water vapor to droplets generates hydrogen peroxide. P Natl Acad Sci USA 2020, 117 (49), 30934-30941. 3. Gallo Jr, A.; Musskopf, N. H.; Liu, X.; Yang, Z.; Petry, J.; Zhang, P.; Thoroddsen, S.; Im, H.; Mishra, H., On the formation of hydrogen peroxide in water microdroplets. Chemical Science 2022, 13 (9), 2574-2583. 4. Musskopf, N. H.; Gallo, A.; Zhang, P.; Petry, J.; Mishra, H., The Air–Water Interface of Water Microdroplets Formed by Ultrasonication or Condensation Does Not Produce H2O2. The Journal of Physical Chemistry Letters 2021, 12 (46), 11422-11429.
BAS/1/1070-01-01
peng.zhang@kaust.edu.sa
Physical Chemistry, Electrochemistry, Analytical Chemistry, Microfluidics, high-speed imaging, chemical kinetics
Physical Chemistry, Electrochemistry, Analytical Chemistry, Microfluidics, high-speed imaging, chemical kinetics
• The intern will conduct a comprehensive experimental investigation of H2O2 formation in (i) water microdroplets sprayed over a range of liquid flow-rates, the (shearing) air flow rates, and the air composition (ii) water microdroplets condensed on hydrophobic substrates formed via hot water or humidifier under controlled air composition. This will also entail a comparative assessment of the various H2O2 detection kits/assays available. • Glovebox experiments will be deployed to quantify H2O2 formation in water microdroplets as a function of the air-borne ozone (O3) concentration. • Effects of atmospherically relevant O3(g) concentrations (10–100 ppb) on the formation of H2O2(aq) will be evaluated. • Effects of the gas–liquid surface area, mixing, and contact duration will be quantified.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
Growth and characterization of 2D materials using sputtering
Academic Program: Electrical Engineering
In this project, the student will be working on the growth of MoS2 using sputtering at different temperatures and characterizing them. The material will be later used in memory devices application.
BAS/1/1698-01-01
nazek.elatab@kaust.edu.sa
MoS2, sputtering, semiconductors, micro and nanofabrication
Electrical engineering, physics, solid-state devices, semiconductors, micro and nanofabrication
Growth of MoS2 using sputtering at different temperatures Characterization of the material - XRD, XPS, Raman, UV-Vis-NIR spectroscopy, AFM
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Dissecting the molecular basis of the neurodevelopmental features associated with Klinefelter syndrome
Academic Program: BioEngineering
Klinefelter syndrome (KS) is the most common chromosome aneuploidy in humans. Our laboratory recently established a unique cohort of KS-iPSCs carrying 47,XXY, 48,XXXY, and 49,XXXXY karyotypes. We apply a disease-modeling approach to investigate the molecular basis of the neurodevelopmental features associated with KS during differentiation of KS-iPSCs into neurons using the most advanced brain-organoids differentiation methods. The Laboratory of Stem Cells and Diseases is seeking an outstanding internship student to work on the study of the role of critical X-linked transcription factors. The selected candidates will combine human iPSC cultures and genome-editing (CRISPR-Cas9) techniques.
BAS/1/1077-01-01
Antonio.adamo@kaust.edu.sa
Disease-modeling, Brain-organoids, Stem Cells, iPSCs
Disease-modeling, Brain-organoids, Stem Cells
The candidate will successfully differentiate disease and healthy iPSCs into disease-relevant tissues applying the most advanced 3D brain-organoids differentiation techniques.
BioEngineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Integrated silicon photonics
Academic Program: Electrical Engineering
Integrated silicon photonics has sparked a significant ramp-up of investment in both academia and industry as a scalable, power-efficient, and eco-friendly solution. At the heart of this platform is the light source, which in itself, has been the focus of research and development extensively. We tackle this from two perspectives: device-level and system-wide points of view. In the former, the different routes taken in integrating on-chip lasers are explored from different material systems to the chosen integration methodologies. In the latter, we seek system-wide applications that show great prospects in incorporating photonic integrated circuits (PIC) with on-chip lasers and active devices, namely, optical communications and interconnects, optical phased array-based LiDAR, sensors for chemical and biological analysis, integrated quantum technologies, and finally, optical computing. By leveraging the myriad inherent attractive features of integrated silicon photonics, we aim to inspire further development in incorporating PICs with on-chip lasers in, but not limited to, these applications for substantial performance gains, green solutions, and mass-production. In this VSRP program, students will have the opportunity to learn the latest device design and fabrication of the integrated silicon photonics chips, and explore the applications based on their own interests. The project needs 3-6 months to be completed. Successful students can likely publish their results in prestigious scientific venues and get enrolled in the Ph.D program in Integrated Photonics Lab.
BAS/1/1700-01-01
alkhazraji_e@jic.edu.sa
integrated Si photonics
photonics Engineering, Physics, Mathematics
The students are expected to acquire the basic knowledge of the design of the integrated silicon photonics chips, proficient skills of device designs using simulation tools, hand-on experimental experiences of optoelectronic device characterizations, and conference/journal publications.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Integration of reservoir simulation with deep learning for subsurface reservoir management
Academic Program: Energy Resources and Petroleum Engineering
DSFT is a research team with diverse expertise including numerical modeling, machine/deep learning and energy system management. We are dedicating to technology development of advanced physics-driven numerical simulation and data-driven modeling for fluid flow in porous media. The goal of this visiting student project is to develop reservoir models to simulate the process of geological carbon storage, geothermal recovery or hydrogen storage, and ultimately use deep learning (e.g., convolutional/recurrent/graphic neural networks) or physics-informed neural networks to establish surrogate models for fast prediction of these nonlinear processes, and ultimately be ready for application of uncertainty quantification and also optimization. We seek for self-motivated, dedicated and creative students who wants to address challenging energy and environmental related engineering problems, whose majors are from petroleum engineering, computational mathematics, machine learning or closely related fields. Desired qualification will be competitive students with good skills of reservoir simulation, python or Julia programming and deep learning.
BAS/1/1423-01-01
bicheng.yan@kaust.edu.sa
Reservoir Simulation, pore-scale simulation, multiphase flow in porous media, deep learning
Petroleum Engineering, Computational Mathematics, Machine Learning
- report - prototype
Energy Resources and Petroleum Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Protein design based on AlphaFold2
Academic Program: Computer Science
AlphaFold2 has made the biggest breakthrough in computational biology and has created the hope to be able to not only solve the forward protein 3D structure prediction problem, but also target a more challenging but more practically useful inverse problem, protein design. Protein design is the core problem in protein engineering and optimization, with a very wide range of applications in enzyme optimization, antibody design, drug development, etc. This project is designed to leverage the power of AlphaFold2 to target the protein design problem through developing AI methods.
BAS/1/1624-01-01
xin.gao@kaust.edu.sa
Protein structure prediction, protein design, deep learning, AlphaFold
AI+Bioinformatics
An end-to-end learning pipeline for protein design.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Computational Bioscience Research Center
3D printing of smart composites for wireless structural health monitoring
Academic Program: Mechanical Engineering
New composite infrastructure used for the energy transition (hydrogen energy, solar, wind) are facing enormous challenges when it comes to integrity and maintenance. We propose here to integrate within smart composites special sensing technologies, allowing the wireless control of these critical systems. The project will make large use of additive manufacturing at different scales.
BAS/1/1315-01-01
gilles.lubineau@kaust.edu.sa
composites, sensors, wireless monitoring, damage in composites
Composites, additive manufacturing, energy, structural integrity
- report - prototype
Mechanical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Techno-economic uncertainty quantification and robust design optimization of hydrothermal and CO2-based geothermal systems
Academic Program: Earth Science and Engineering
The viability and sustainability of geothermal energy development are subject to several reservoir (subsurface) and economic parameter uncertainties. Determining the optimal operational parameters of the geothermal system, in the presence of uncertainty, require hundreds, if not thousands, of permutations of the different uncertain or naturally variable reservoir, operational and economic parameters for any specific hydrothermal system. The key task of this project is to quantify reservoir and economic uncertainties and to examine their effects on the techno-economic performance of the hydrothermal doublet system. What are the ranges of permissible reservoir conditions (parameters) to render a geothermal system economically viable? What are the trade-offs between these parameters? What are the ‘acceptable uncertainties’ and the ‘optimized values’ for a geothermal-energy producer in the economic decision-making? These kinds of questions will be addressed in this project, by means of scanning the related parameter-space via a very large number of numerical simulations, to then derive first-order ‘reduced model metrics’ that help to derive simplified decision-making quantities. In this study, we will develop a novel generalized non-dimensional expression of power generation over time to characterize the optimal operational parameters for various combinations of the reservoir parameters, and to determine which combination of the parameters depletes the reservoir heat the fastest. This methodology will further be applied to evaluate and compare the performance of geothermal systems that use in-situ brine or (sequestered) CO2 as the heat-extraction fluid, with emphasis on Saudi Arabia’s geological and economic conditions. Finite element/volume simulators such as TOUGH2/3, DARTS, or CMG, and wellbore-power system models will be used to conduct comprehensive simulations and to stochastically assess the system’s power and lifetime based on a range of available subsurface parameters (e.g., depth, thickness, permeability, porosity, permeability anisotropy), operational parameters (e.g., flow rate, injection temperature, well spacing) and economic parameters (e.g., drilling cost, heat cost, electricity cost, etc.). The candidate must have the willingness to learn basic geoscience and reservoir engineering concepts. Affinity with programming (preferably MATLAB/Python) and Paraview or related visualization tools is an advantage.
BAS/1/1339-01-01
chima.ezekiel@kaust.edu.sa
geothermal energy, techno-economic, uncertainty quantification, CO2 utilization and storage, optimization, energy systems, reservoir engineering
Earth Science and Engineering - Geothermal energy and Co2 utilization and geological storage
The intern will (i) carry out a literature study to compile a range of relevant subsurface inputs, (ii) build a parameterizable 3D reservoir model for the simulation of the geothermal system, (iii) develop an economic model for geothermal energy development in Saudi Arabia and, (iv) perform numerical simulations for optimization and uncertainty quantification of hydrothermal and CO2-based geothermal systems.
Earth Science and Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Digital Outcrop Model-based analysis of fracture network
Academic Program: Earth Science and Engineering
The presence of hydraulically conductive fracture networks is a key parameter influencing the fluid flow in hydrocarbon reservoirs, affecting the ultimate recovery factor, productivity and future development planning. This project aims to map and measure the fracture network in the Duqm area (Shuaiba Formation, Sultanate of Oman) using 3D and 2D photogrammetry data. We will analyze fractures at different scales to extract quantitative information about the spatial organization, the intensity and the variability with respect to the main faults observed in the area. For this project we are looking for a motivated geology student with a solid background in structural geology.
BAS/1/1399-01-01
yuri.panara@kaust.edu.sa
Geology, Structural Geology, Digital Photogrammetry, Fracture network
Structural geology
Define fracture set and fracture intensity variation of the area in relation with the main structural features.
Earth Science and Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Capturing adhesion molecules in action through imaging
Academic Program: BioScience
Cell adhesion occurs through spatio-temporally regulated interactions that are mediated by multiple intra- and inter-cellular components. Physiologically, shear forces on flowing cells orchestrate these interactions. The conventional assay used to study the effect of shear flow on cell adhesion is the parallel plate flow chamber (PPFC) assay, which records videos of cells rolling in flow and adhering to adhesion molecules on cells (eg. endothelial) or immobilized to a surface. However, due to the limited spatial resolution and sensitivity of these assays, nanoscopic molecular level mechanisms of selectin-selectin ligand interactions and their role in leukocyte (neutrophils, HSCs, T-cells) migration can’t be assessed. Recent developments in super resolution and single-molecule fluorescence imaging techniques allow for the visualization of individual molecules with nanometer spatial resolution and millisecond temporal resolution. Furthermore, advanced super-resolution microscopy provides unique opportunities to obtain information about nanometer-scale conformational dynamics of protein complexes as well as nanoscale architectures of biological samples. In collaboration with S. Habuchi (BESE, KAUST), whose research focuses on the development of tools and materials for fluorescence molecular imaging, we optimized the PPFC assay to image the cell using super resolution fluorescence microscopy. This allows us to image single molecular ligand architecture on the cell surface and determine how the ligand distribution was influenced as a result of rolling on selectin (E-/P-selectin) surfaces, both to each other (clustering) and to other selectin ligands. To characterize the dynamics of ligand distribution, we also developed real-time live cell imaging of these ligands under shear flow and are able to beautifully observe long, thin, flexible structures protruding out from the rear (tethers) and the front (slings) sides of the cell as it rolls over selectin expressing surfaces. For this project, these novel-imaging tools, combined with molecular, cellular and proteomic technologies will be used to further understand the cellular landscape that results before, during and following the migration of model cells such as hematopoietic stem cells.
BAS/1/1005-01-01
jasmeen.merzaban@kaust.edu.sa
Selectins, cell migration, metastasis, glycobiology
Cell Biology and Imaging
- overall goal is to develop a platform for imaging cells in flow over tissue samples expressing selectins - growth and maintenance of cell lines and primary cells - prepare and stain tissue sections for selectins (E-/P-/L-selectin) - prepare and stain cells lines for selectin ligands - run flow assays and use super resolution imaging under the supervision of an experienced PhD student
BioScience
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Machine learning for wireless communication systems
Academic Program: Electrical Engineering
At the CCSL, we are engaged in research and teaching on wireless communication methods for future wireless communication systems. In future wireless communication 5G and beyond, an extremely high number of heterogeneous devices, such as smartphones, sensors, robots, and vehicles, will communicate with each other. Consequently, the need for higher data rates and lower latency will increase significantly, posing major challenges for the resource allocation. Deep learning and data-driven algorithm approximation schemes have recently received significant attention as means to perform resource allocation with reduced complexity in 5G and beyond networks. This project considers the challenging case of Reflective Intelligent System (RIS) assisted, mmWave, frequency selective, massive MIMO systems with hybrid architecture and develops deep-learning based resource allocation frameworks. In these frameworks, prior data-set observations and deep neural network models will be leveraged to learn the mapping from received measurements to channels, beamformers and power allocations. Furthermore, deep neural networks will be used to approximate the optimization problems by selecting the suitable parameters that minimize the approximation error. The usage of a deep neural network framework reduces the computational complexity and processing overhead, since it only requires a limited number of layers of matrix-vector multiplications which can reduce processing time substantially.
BAS/1/1686-01-01
ahmed.eltawil@kaust.edu.sa
Machine Learning, MIMO, Wireless, ML
Electrical and Computer Engineering
Goal: This project aims to improve the overall performance on emerging and beyond 5G wireless systems in boosting system capacity with improved robustness and high data rates, via a low-cost, low-latency, and green implementation. For this purpose, deep learning or machine learning methods shall be applied, which can adapt to the dynamic changes of the wireless communication system and its environment and exploit the past experience to improve the future performance of the system. A report detailing system design and simulation results are expected at the end of the program.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Micro-devices for AR and VR head-mounted displays
Academic Program: Electrical Engineering
Micro-LED displays or laser displays will be useful for head-mounted displays for virtual reality (VR) and augmented reality (AR). This project aims to develop micro-LEDs and vertical-cavity surface-emitting lasers (VCSELs). Those devices are made from nitride semiconductors grown by the original MOVPE method at KAUST. Also, study material science of nitride semiconductors and their quantum structures.
BAS/1/1676-01-01
kazuhiro.ohkawa@kaust.edu.sa
Optoelectronics, Semiconductor, LED, LD, VCSEL, laser, micro-LED, Physics, Electronics
Optoelectronics
Realizing novel micro-LEDs and VCSELs in RGB, especially the red region is the most difficult topic for worldwide scientists and researchers.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Identifying novel Cas variants for pathogen diagnostics
Academic Program: BioEngineering
Rapid, point-of-care (POC) diagnostics are essential to mitigate the impacts of current (and future) epidemics; however, current methods for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) require complicated laboratory tests that are generally conducted off-site and require substantial time. CRISPR-Cas systems have been harnessed to develop sensitive and specific platforms for nucleic acid detection. These detection platforms take advantage of CRISPR enzymes’ RNA-guided specificity for RNA and DNA targets and collateral trans activities on single-stranded RNA and DNA reporters. Microbial genomes possess an extensive range of CRISPR enzymes with different specificities and levels of collateral activity; identifying new enzymes may improve CRISPR-based diagnostics. We work to identify new Cas variants, and characterize its catalytic activity.
BAS/1/1035-01-01
norhan.hassn@kaust.edu.sa
Bioengineering, diagnostics, CRSIPR
Bioengineering
Training on different molecular biology techniques and comprehending the components and mechanisms of the CRSIPR-Cas systems.
BioEngineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Investigating the carrier dynamics of emerging wide band gap semiconductor for novel optoelectronic applications
Academic Program: Marine Science
Academic Program: Materials Science & Engineering
Studying fundamental sciences is the key factor for technological development, as this allows the researchers to better understand the natural phenomena behind human discoveries. In the context of semiconductor innovation, for example, the physics principles of carrier recombination and the importance of quantum mechanics, including carrier quantum confinement, localization, and their effects on the carrier wavefunction, need to be studied in order to ensure efficient semiconductor-based devices. Mainly, technology based on wide bandgap semiconductors as high-energy optoelectronics based on these materials that operate at the deep UV and UV spectral ranges got scientists attention due to their use for many applications in different fields, such as medical treatment, astronomy investigations, material analysis, missile detection, space communications, security systems, and x-ray imaging. Due to the lack of suitable substrates, the emitting devices still need further enhancement. For example, no commercial laser diode operates in the UV spectral range below 375 eV. Therefore, in this project, this issue will be addressed to enhance the emission of these materials. The project will focus on studying the fundamental physics of the carrier dynamics of wide-bandgap semiconductors such as III-nitrides and oxides. Several structures will be investigated experimentally and theoretically for developing wide bandgap semiconductor-based materials, one of which is carrier confinement using different novel approaches. The project goals will be achieved by employing time-integrated photoluminescence (PL) using CW lasers as well as time-resolved PL using ultrafast oscillators attached to streak camera or photon-counting detection systems. In addition, PL excitation will be used to understand the origin of the emitted light using a Xe lamp attached to a fluorescence system. The theoretical simulation of emitting devices will be carried out by analytical code, such as Lumerical.
BAS/1/1319-01-01
iman.roqan@kaust.edu.sa
Wide bandgap semiconductor, LED, laser diode, emitting devices, DUV
Optical spectroscopy of semiconductors
- Be familiar with advanced optical spectroscopy techniques - Understanding the fundamentals of optical properties of semiconductor - Understanding the carrier dynamics of semiconductor - Achieving theoretical and experimental results of the novel semiconductor structure - Analyzing the carrier dynamic outputs - Finally, concluding the desired projects
Marine Science
Physical Sciences and Engineering
Graduate or Undergraduate
Organic Solvent Nanofiltration
Academic Program: Chemical Engineering
Separation processes play a remarkable role in the chemical and pharmaceutical industries, where they account for 50 to 70% of both capital and operational costs. Organic synthesis in the chemical and pharmaceutical industry are frequently performed in organic solvents and consist of products with high added value that should be removed from the organic solvents. Organic solvent nanofiltration is an emerging technology which allows the isolation and separation of solutes between 50 and 2000 g/mol in organic solvents. The development of nanofiltration membranes stable in harsh environments (e.g. polar aprotic solvents, extreme temperature, pressure and pH) is of utmost importance. Solvent resistant nanofiltration membranes will be fabricated exhibiting superior chemical stability compared to commercial polymeric membranes. The concept of design of experiments (DoE) will be applied throughout the project to gain an in depth understanding parameters governing the membrane will be prepared and crosslinked using aromatic bifunctional crosslinkers. The membrane performance (i.e. flux, retention profile, solvent stability) will be evaluated using a nanofiltration rig.
BAS/1/1401-01-01
gyorgy.szekely@kaust.edu.sa
Organic solvent nanofiltration, membrane fabrication, membrane separations, hybrid processes
Engineering in Pharmaceutical Industry
The student will acquire soft skills such as team working, project and time management, giving oral presentations. By the end of the traineeship the student will have a deep understanding of membrane separations, particularly in nanofiltration. Practical and theoretical aspects of surface modification techniques and polymer chemistry techniques will be acquired.
Chemical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Safeguarding our daily bread from wheat rust diseases
Academic Program: Plant Science
Wheat rusts are destructive diseases of wheat, which throughout recorded history have caused devastating epidemics almost wherever wheat is grown. The wild ancestors of domesticated wheat represent a rich source of genetic variation with huge potential for improving disease resistance. Deploying this genetic diversity into elite, cultivated wheat by traditional breeding takes many years for just a single resistance gene. However, the molecular identification (cloning) of resistance genes opens up new possibilities for accelerated breeding by marker-assisted selection and genetic engineering [Refs. 1,2]. The Wulff lab has established a suite of molecular plant breeding technologies that significantly reduce costs and accelerate plant growth [Refs. 3,4], gene discovery [Refs. 5,6,7,8,9] and gene cloning [Refs. 7,8,9,10]. You will use our tools, structured germplasm, and sequence resources to characterize novel candidate rust resistance genes. You will be supervised by Brande Wulff and receive training and co-supervision from a team of Postdocs and PhD students with expertise in bioinformatics, mathematics, scripting, genetics, plant pathogen interactions, wheat husbandry and crossing. KAUST is a vibrant place to discuss and plan science. You will become part of the larger KAUST community and alumni, which we hope will have lasting positive impacts on your future career. References [1] Dhugga & Wulff (2018). Science 361:451-452. [2] Luo et al (2021) Nature Biotechnology 39:561-566. [3] Watson et al (2017) Nature Plants 4:23-29. [4] Ghosh et al (2018) Nature Protocols 13:2944-2963. [5] Steuernagel et al (2015) Bioinformatics 31:1665-7. [6] Steuernagel et al (2020) Plant Physiology 183:468-482. [7] Arora et al (2018) Nature Biotechnology 2:139-143. [8] Gaurav et al. (2020) bioRxiv doi.org/10.1101/2021.01.31.428788. [9] Steuernagel et al (2016). Nature Biotechnology 34:652-5. [10] Sánchez-Martín et al (2016). Genome Biology 17:221.
BAS/1/1091‐01‐01
brande.wulff@kaust.edu.sa
Wheat, wheat rust, resistance genes, GWAS, bioinformatics, plant disease, cloning, food security
Plant genetics
Research experience including learning of one or more techniques employed in the lab, the generation of original data, design of figure(s), and presentation of results at lab meeting.
Plant Science
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Center for Desert Agriculture
Design and 3D print a continuous flow reactor
Academic Program: Chemical Engineering
High-added value chemicals and active pharmaceutical ingredients (APIs) are normally produced using batch technology. Some of those chemical compounds can be produced in continuous flow reactors (i.e. continuous manufacturing). Advanced techniques of manufacturing like 3D printing allow us to produce specific reactors that are optimized to produce a certain chemical or pharmaceutical component. Such novel designs are of importance when heat of reaction needs to be evacuated faster or when improved mixing is required, etc. The current project aims to use digital tools to produce custom designs of continuous flow reactors. The student will also be involved in the production of the reactor and on its flow pattern characterization (determination of residence time distribution).
BAS/1/1420-01-01
carlos.grande@kaust.edu.sa
reaction: 3D printing: simulation: digitalization
reaction engineering ; transport phenomena ; digitalization
Learn how to produce novel designs and transform them into high quality meshes for 3D printing. Rhino3D and Grasshopper will be used for parametric geometry design. Categorize the advantages, but also the limitations of 3D printing as a manufacturing tool for chemical reactors. Understand residence time distribution concepts and participate in a joint scientific publication.
Chemical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Advanced Membranes and Porous Materials Center
Modelling tools for advanced separation processes
Academic Program: Chemical Engineering
Adsorption processes are emerging separation technologies that have the potential to cut energy consumption in many different separations vital to society. Differently from other separation technologies, adsorption processes are transient processes where the operation of a bundle of columns is synchronized to operate in an efficient manner. Modelling the performance of adsorption processes involve the utilization of advanced modelling tools that will be learnt in this project. The student will learn how to develop a mathematical model for a given gas separation and to use gPROMS language as a tool to numerically solve this problem. The main objective of this project is to learn how to model adsorption processes in general so there is plenty of flexibility in selecting the separation to be targeted.
BAS/1/1420-01-01
carlos.grande@kaust.edu.sa
adsorption: separation: 3D printing: simulation: digitalization
separation processes ; transport phenomena ; modelling
Learn to use gPROMS software and how to transform equation-based problems into computer code. Understand the important variables that are important for simulation and optimization of adsorption processes. Participate in a joint scientific publication.
Chemical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Advanced Membranes and Porous Materials Center
Modeling Fluid Flow and Transport in Porous Media by Physics-driven Simulation Approaches
Academic Program: Energy Resources and Petroleum Engineering
The Digital Subsurface Flow & Transport Lab (DSFT-Lab) is a cross-discipline team led by Prof. Bicheng Yan KAUST. Currently DSFT focuses on physics-driven and data-driven (deep learning mainly) model development that can simulate multiphase flow in porous media at reservoir scale and pore scale. The goal is to explore the fundamental physics that governs the complex and coupled physics related flow and transport in porous media, and investigate its impact on subsurface fluid flow such as hydrocarbon recovery, geologic carbon sequestration, hydrogen storage and geothermal recovery etc. DSFT seeks for self-motivated, dedicated and creative STEM-majored students who wants to address challenging energy and environmental related engineering problems.
BAS/1/1423-01-01
bicheng.yan@kaust.edu.sa
Reservoir Simulation, pore-scale simulation, multiphase flow in porous media, deep learning
Subsurface Modeling
* Subsurface modeling workflow/software for reservoir or pore scale modeling; * 1 to 2 publications based on the scientific findings.
Energy Resources and Petroleum Engineering
Physical Sciences and Engineering
Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Fully 3D Printed Flexible ECG Patch with Dry Electrodes
Academic Program: Electrical Engineering
The project aims to design and develop a fully 3D Printed Flexible ECG Patch with Dry Electrodes. The student will work on the design and 3D printing of different conductive materials such as graphene and carbon nanotubes, and testing them as ECG dry electrodes. The student will be characterizing the reliability of the electrodes using a semiconductor device analyzer ad probe station, in addition to the endurance of the electrodes after performing several bending, twisting and stretching cycles.
BAS/1/1698-01-01
nazek.elatab@kaust.edu.sa
3D printing, ECG, flexible electronics
3D Printed Electronics
1- Design and 3D printing of ECG electrodes and substrate based on different materials combinations 2- Electrical (skin impedance) and mechanical testing of the electrodes (bending, stretching, and twisting cycling tests) 3- Testing of the comfort to the wearer
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate
Efficient pricing of high-dimensional (multi-assets) European Options
Academic Program: Applied Mathematics and Computer Science
The student will work on designing new numerical methods based on hierarchical adaptive sparse grids quadratures combined with Fourier techniques for efficient pricing of high-dimensional (multi-assets) European Options. Specifically, the student will contemplate the possibility of finding a heuristic framework for an optimal choice of the integration contour (damping parameter) which controls the analyticity of the integrand in the Fourier space and hence accelerate the performance of the quadrature methods. He will also develop a systematic comparison between hierarchical deterministic quadrature methods, Tensor Product (TP) quadrature, Smolyak (SM) Sparse Grids quadrature, and Adaptive Sparse Grids (ASG) quadrature to numerically evaluate the option price under different pricing dynamics, Geometric Brownian Motion (GBM), Variance Gamma (VG) and Normal Inverse Gaussian (NIG) for different multi-asset payoff functions such as Basket Call/Put and Rainbow options. The student is also asked to elaborate a comparison in terms of computational complexity against the quadrature methods for different dimensions, and various combination of parameter sets within the mentioned pricing models.
400000024
chiheb.benhammouda@kaust.edu.sa
Multi-Asset Option Pricing, Fourier Transform, Lewis Valuation Approach, Damping Parameters, Lévy models, Hierarchical Adaptive Sparse Grids Quadrature, Monte Carlo
Computational Finance, Computational Mathematics, Numerical Analysis
As the main project deliverable, we expect a scientific report (eventually a research manuscript) including a detailed description and analysis of the proposed methodology developed within the course of the internship and providing all numerical experiments to showcase the versatility of the proposed heuristic framework. The working environment the student will use should include a GIT repository shared with the project collaborators in which he includes all project-related materials such as progress reports, codes, figures, and important references from the literature to facilitate the supervision task and communicate ideas more effectively.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Learning to model geophysical processes with NNs
Academic Program: Earth Science and Engineering
Scientific machine learning (SML) is an emerging field focused on leveraging machine learning across different sciences whose natural processes are governed by well-established physical equations. Early successes have been reported by the geophysical community in terms of modelling various wave phenomena by means of PINNS, e.g. eikonal equation (bin Waheed et al., 2021) and wave equation (Alkhalifah et al., 2021). Alternative solutions have recently emerged in the SML community with the aim of overcoming some of the limitations of PINNs and extending applicability from functionals to operators. This project will investigate such approaches in the context of geophysical modelling and inversion and identify the benefits and limitations when compared to traditional modelling methods as well as PINNs. References: - U. bin Waheed, E. Haghighat, T. Alkhalifah, C Song, Q Hao, 2021, PINNeik: Eikonal solution using physics-informed neural networks, Computers & Geosciences. - T. Alkhalifah, C. Song, U. Waheed, Q. Hao, 2021, Wavefield solutions from machine learned functions, arXiv preprint arXiv:2106.01433.
BAS/1/1414-01-01
matteo.ravasi@kaust.edu.sa
geophysics; modelling; machine learning;
Geophysics
The candidate will be tasked with: - Literature review of ML methods for learning physical phenomena governed by PDEs. - Develop and implement one of the methods for a geophysical PDE of choice. - Compare pros and cons of the developed method against state-of-the art numerical modelling methods and PINNs.
Earth Science and Engineering
Physical Sciences and Engineering
Undergraduate
Resilient Models for Attacks Detection in Cyber-Physical Systems
Academic Program: Electrical Engineering
The goal of this project is developing methods to merge Machine Learning (ML) with physics-based models to create control algorithms can significantly enhance the resiliency of Cyber-Physical Systems (CPS). The approach will combine recent results in ML with control theory via constrained optimization to create novel systems and methods for protecting CPS from malicious cyber intruders via detection and prevention strategies. Specifically, the research project focuses on (1) refining the offline/online training and execution algorithms of ML models through physics-based constrained optimization, (2) developing secure estimation and control algorithms that are significantly more resilient to cyber-attacks than the state-of-the-art counterparts, and (3) improving the distributed resiliency for networked systems supporting multi-agent autonomous systems.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
Cyber-physical systems, resiliency, cybersecurity, control methods, machine learning, physics-based methods.
Electrical and Computer Engineering/Computer Science
The goal of this internship is to analyze and improve existing data-driven and physics-based algorithms for attack detection in CPS. The student will be expected to learn about existing solutions, as well as the challenges and requirements to applying such techniques in their settings. With guidance of other team members, the student will then find new solutions for improving algorithmic resiliency in order to reduce cyber-risks related to the CPS operation. Candidates should be motivated to work on research-oriented problems with a team and develop new solutions. They should have a strong background in controls, in particular with regards to machine learning and power systems. They are also expected to be proficient in MATLAB/Simulink.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Real-Time (co-)Simulation for Cybersecurity
Academic Program: Computer Science
The objective of this project is to use Real-Time Simulation (RTS) and co-simulation models for real-time prediction and control of disturbances related with attacks propagation. Essentially, the goal is to study how RTS via real-time communication interfaces of different RTS tools can run systems security, resilient and isolation algorithms in order to monitor and control the system under study.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
Real-time simulation, cybersecurity, attacks, detection, prevention.
Electrical and Computer Engineering/Computer Science
The goal of this project is to develop and implement models in RTS using OPAL-RT hardware and software to study how cyber-attacks effects can propagate in real-time and investigate controls able to isolate such impact. Students should have backgrounds and experience using real-time simulators software such as RT-LAB, HYPERSIM, RSCAD, etc.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Regaining Trust in IoT
Academic Program: Computer Science
While embedded devices play an increasingly significant role in the functional interoperability and coordination of various IoT systems, they are often developed without security in mind. The premise of this research is that effective security solutions can benefit from assistance by a trustworthy hardware root of trust. This research involves approaches that utilize device intrinsic (non-)silicon-based physical/hardware characteristics to verify, authenticate, and trust IoT devices. The goal is for such methods is to support and improve detection of security breaches in the device, network, or/and system level within critical information infrastructures. This can be achieved by leveraging the acquired hardware-level values in order to verify the integrity of each layer in a single device as well the integrity of the process variables within a control operation.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
IoT, security, embedded systems, hardware security.
Computer Science and Engineering
1. Experimental evaluation of an existing hardware-based security approach. 2. Design and implementation of an improved approach incorporating a mixture of hardware signals: build an intrusion detection model capturing any malicious activity in any layer of the system stack.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Cyber-Secure Integration of Renewable Energy Sources
Academic Program: Electrical Engineering
This research project aims to contribute towards a resilient and secure power system employing power electronics for renewable energy sources (RES) integration. The goal is to examine the security implications of integrity and availability attacks in power transmission and distribution systems, and examine the performance and economic impact on real-time energy market operations. Risk assessment methodologies will quantify attack resources under certain level of adversarial knowledge. The objective is to enhance system cyber-(resiliency, security), while addressing disruptive cyber-physical events and preventing these effects from escalating into major failures and hence causing cascading blackout scenarios.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
Renewable energy, cybersecurity, power electronics, prevention, cyber-physical energy systems.
Electrical and Computer Engineering/Computer Science
1. Improve an existing power system benchmark to support certain RES and their corresponding controllers. 2. Impact evaluation of attacks at the various connection points. 3. Examine effects of attacks on market operations. 4. Identify methods to improve system performance.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Ransomware in Industrial Control Systems
Academic Program: Computer Science
According to a recent report, ransomware attacks on industrial entities increased more than 500% from 2018 to 2020. What is more, in 2020, ransomware, targeted ransomware, supply chain breaches and cloud connectivity all emerged as top-of-mind concerns for security teams at industrial enterprises. One of the biggest cyberattacks in history – the SolarWinds Orion supply chain breach – impacted as many as 18,000 organizations, many of which were industrial enterprises with physical operations. As a result, this project will study effective defense-in-depth security strategies, ensure an understanding of network interdependencies, and conduct crown jewel analysis to identify potential weaknesses that could disrupt business continuity and production in the event of ransomwares.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
Ransomware, industrial control systems, cybersecurity, critical infrastructure.
Computer Science and Engineering
In this project, the students will examine the security issues of supply chain ransomware in industrial control systems (ICS) environments. One direction would be to investigate the applicability of unidirectional gateway technology to provide robust protection from such targeted attacks. Other methods include investigation of deep learning malware detector indicators.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Machine learning techniques for divergence-free field reconstruction
Academic Program: Applied Mathematics and Computer Science

The student will work on machine learning techniques applied to the study of divergence-free flow reconstruction. Specifically, the student will use different Neural Network architectures and training algorithms to reconstruct a divergence-free flow from sparse and noisy data. The student will also investigate the spectral properties of the reconstructed flow and use this information to improve the training algorithm. They will test the methods on several problems and compare results with existing methods. We will meet weekly during the duration of the project.

BAS/1/1604-01-01
raul.tempone@kaust.edu.sa
Machine Learning, Deep Neural Networks, Dynamical Systems, Numerical Analysis, Stochastic Numerics
Machine learning
As the main project deliverable, we expect a scientific report describing the methodology developed in the internship and its numerical use in various applications. The working environment the student will use should include a GIT repository for all project-related materials to facilitate proper verification processes. These materials include, among others, the codes and the saved input-outputs corresponding to all tested cases.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate
Machine Learning and Dynamical Systems
Academic Program: Applied Mathematics and Computer Science
The student will work on machine learning techniques applied to the study of dynamical systems. Specifically, the student will use different Neural Network architectures to approximate the equations governing a given system's evolution. This evolution may be intrinsically random, or we may add randomness to contemplate the possibility of approximating a large dimensional system by a low dimensional one. He will also test this methodology in several applications and report his results to get familiar with the problem and its input data.
4000000024
erik.vonschwerin@kaust.edu.sa
Machine Learning, Deep Neural Networks, Dynamical Systems, Numerical Analysis, Stochastic Numerics
Computational and Applied Mathematics
As the main project deliverable, we expect a scientific report describing the methodology developed in the internship and its numerical use in various applications. The working environment the student will use should include a GIT repository for all project-related materials to facilitate proper verification and feedback processes. These materials include, among others, the codes and the saved input-outputs corresponding to all tested cases
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Developing bioinformatic tools for Multi-omic data integration
Academic Program: BioScience
The past two decades have provided unprecedented growth in data modalities and data availability within the biomedical research. As a result, methodologies for the multi-omic analysis of bulk (average profiles derived from hundreds to millions of cells), single-cell data or both are in continuous development. Our team has been involved in such developments working on multi-omic frameworks (STATegra framework + STATEgRa Bioconductor package), multi-omic DeepLearning based analysis (LIBRA - BioarXiv) and specific tools such as GeneSetCluster to summarize information derived from gene-set enrichment analysis. We are looking for highly motivated and skilled visiting students to work on one or several of the following challenges sub-projects: - Upgrading STATegRA Bioconductor package to account for the novel developments, including the single-cell analysis applications and the integration with GeneSetCluster analysis tool. - Novel Deep Learning-based methodologies for multi-omic analysis. - Implementing Cell-to-cell interaction analysis as part of a multi-omic integrative framework. We have existing proprietary data to work in the context of the Bone Marrow. For any selected student, the project to be conducted will be decided based on the student's interest, technical proficiency, and level of study. We expect for any participant (a) to bring motivation, enthusiasm, creativity, and hard work, (b) give lab seminars on your work, (c) collaborate with other lab members, and (d) produce a final written report. References of interest: - STATegra framework: https://www.frontiersin.org/articles/10.3389/fgene.2021.620453/abstract - STATegRa package: https://www.bioconductor.org/packages/release/bioc/html/STATegRa.html - LIBRA single-cell multi-omic framework: https://www.biorxiv.org/content/10.1101/2021.01.27.428400v1 Link to recent publications of the team: http://www.lunacab.org/publications/
BAS/1/1093-01-01
David.gomezcabrero@kaust.edu.sa
Single-cell analysis, multi-omic analysis, Deep Learning,
Biosciences, bioinformatics
Enhancing critical thinking, presentations skills, and scientific writing. The research, in collaboration and with support of team members, may lead to scientific publications. Obtaining a hands-on perspective at the frontier of bioinformatics, multi-omic analysis, and its applications in an interdisciplinary research group and environment.
BioScience
Biological and Environmental Sciences and Engineering
Undergraduate
Bringing Chromatin Conformation and Spatial profiling into clinical research
Academic Program: BioScience
The past two decades have provided unprecedented growth in data modalities and data availability within the biomedical research. As a result, methodologies for the multi-omic analysis of bulk (average profiles derived from hundreds to millions of cells), single-cell data or both are in continuous development. Our team has been involved in such developments working on multi-omic frameworks (STATegra framework + STATEgRa Bioconductor package), multi-omic DeepLearning based analysis (LIBRA - BioarXiv) and specific tools such as GeneSetCluster. As a next step, we aim to integrate into current integrative pipelines additional data-types such as Chromatin Conformation and Spatial profiling. We are looking for highly motivated and skilled visiting students to work on one or several of the following challenges sub-projects: - Integrating Chromatin Conformation information (such as HiC) in clinical oriented research. Myeloma as a case study. Proprietary data is available for such integration. - Integrating Spatial Transcriptomics and Cell-to-cell interaction analysis as part of a multi-omic integrative framework. We have existing proprietary data to work in the context of the Bone Marrow. For any selected student, the project to be conducted will be decided based on the student's interest, technical proficiency, and level of study. We expect for any participant (a) to bring motivation, enthusiasm, creativity, and hard work, (b) give lab seminars on your work, (c) collaborate with other lab members, and (d) produce a final written report. The project relies on multi-skilled collaborations involving biologists, clinicians, bioinformaticians and computational biologists. References of interest: - STATegra framework: https://www.frontiersin.org/articles/10.3389/fgene.2021.620453/abstract - STATegRa package: https://www.bioconductor.org/packages/release/bioc/html/STATegRa.html - LIBRA single-cell multi-omic framework: https://www.biorxiv.org/content/10.1101/2021.01.27.428400v1 - 3D data: https://www.nature.com/articles/s41467-020-20849-y
BAS/1/1093-01-01
David.gomezcabrero@kaust.edu.sa
Single-cell analysis, Multi-omic analysis, Deep Learning, Chromatin Conformation, Spatial Transcriptomics, Translational research
Biomedicine
Enhancing critical thinking, presentations skills, and scientific writing. The research, in collaboration and with support of team members, may lead to scientific publications. Obtaining a hands-on perspective at the frontier of bioinformatics, multi-omic analysis, and its applications in an interdisciplinary research group and environment.
BioScience
Biological and Environmental Sciences and Engineering
Undergraduate
Numerical approximation of partial differential equations
Academic Program: Applied Mathematics and Computer Science
The numerical approximation of partial differential equations is a relevant field of applied mathematics. Several projects are available at my research group, ranging from purely theoretical studied to more applied and computational tasks. Theoretical project will involve the study and analysis of the properties of the approximation of some partial differential equations. Computational projects are related to the implementation and testing of research codes for scientific computing. The areas of applications include electromagnetism, structural mechanics, fluid-dynamics, fluid-structure interactions.
BAS/1/1688-01-01
daniele.boffi@kasut.edu.sa
Numerical Analysis, Applied Mathematics, Scientific Computing
Numerical analysis, Applied Mathematics, Scientific Computing
Implementation and testing of the proposed algorithms. Study and analysis of the properties of approximating schemes for partial differential equations.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Causal and Fair Machine Learning
Academic Program: Computer Science
The exact topic depends on the student's interest, student's background, and previous research experience. Generally speaking, there are mainly three topics of this project: 1. Designing Fair Machine Learning Algorithms. In this project, students will focus on how to make the current machine learning algorithms be fair. They will also explore the fairness issue of the current machine learning algorithms, especially for healthcare data. 2. Causality as a tool for de-biasing current deep learning algorithms. Students will use the idea of causality to different deep learning tasks to de-bias the datasets or algorithms in order to improve the accuracy and trustworthiness. 3. Causality as a tool for invariant learning. This project mainly focuses on transfer learning, students will use causality to design transfer learning algorithms.
BAS/1/1689-01-01
di.wang@kaust.edu.sa
Machine Learning, Deep Learning
Causal Inference, Fairness, Transfer Learning, Deep Learning
During the project, students will have opportunity to learn about some topics in trustworthy machine learning, especially fair learning, transfer learning and causal learning. They will learn and implement the SOTA methods. Hopefully, they may produce some publication after the intern.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Robust/Differentially Private Machine Learning
Academic Program: Applied Mathematics and Computer Science
The topic is flexible and depends on student's background, mathematical knowledge, previous research experience. Generally, this project mainly focuses on how to design robust (especially robust against to outliers or heavy-tailed distributions) or private (or forgettable) algorithms for some foundamental problems in machine learning, deep learning or statistics. Students will provide theoretical guarantees via using mathematical tools from probability, learning theory, optimization and high dimensional statistics. Also, student will analyze utility-privacy tradeoff or robustness-utility tradeoff.
BAS/1/1689-01-01
di.wang@kaust.edu.sa
Machine Learning, Privacy, Statistics
Machine Learning, Data Privacy, High Dimensional Statistics
Students will learn some fundamental techniques and results in learning theory, high dimensional statistics, optimization and differential privacy. They will also implement machine learning or statistics algorithms via using Matlab or Python. Hopefully they could have publications after the project.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Foundations of Private and Fair Statistics
Academic Program: Statistics
The topic is flexible and depends on student's background, previous experience and mathematical skill. Specifically, in the topic, students will explore basic statistical model or problems for different types of data in the differential privacy or fairness model. For statistical model , topics include component analysis, supervised learning, mixture model , et al. For data, topics include survival data, functional data, network analysis. Students will design new algorithms to solve these problems and provide theoretical guarantees on the utility-privacy or utility-fairness tradeoff. Moreover, they will implement these algorithms on some sensitive data such as healthcare and biomedical data.
BAS/1/1689-01-01
di.wang@kaust.edu.sa
Machine Learning, Statistics, Privacy
Machine Learning, Data Privacy, Statistics
Students will learn basic techniques and terminologies in differential privacy, fairness and some topics in statistics. They will have the chance to implement the best-known algorithm for some specific problems. Hopefully, they could have some new results and publications after the project.
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Protocol development for biomass quantification in membrane autopsies
Academic Program: Environmental Science and Engineering
A membrane autopsy is a valuable tool used in membrane (bio)fouling studies. One of the objectives of the autopsy is to quantify the amount of biomass. The current method involves sonication of the sample, followed by filtration and DOC measurement. The objective of the project is to investigate the influence of the sample preparation method on the measured value. 
BAS/1/1024-01-01
Graciela Gonzalez Gil
Membrane filtration, biofouling, chemical analysis
Chemical & Biological engineering
The student should critically evaluate the current method, and propose or develop improvements/alternatives.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Undergraduate
Water Desalination and Reuse Center
Body Area Networks
Academic Program: Electrical Engineering
Students will be engaged in design, simulation and evaluation of a body area network using on-skin communication modules. Accurate, real time sensing of body vital functions leads to better preventative medicine approached and more accurate diagnosis. State of the art wireless solutions, such as Bluetooth, attempt to provide such functionality, however their relatively large size and power demands limited their widespread use for personal telemetry. This project advocates the use of a new communication channel instead of air, which is the “human body” itself. Our vision is to provide a network of smart mini-distributed body mounted sensors that can perform personal telemetry and connect to the external world wirelessly using a central hub. The sensors themselves do require a radio frequency section or antennas since they directly interface to the skin (similar to smart watches) and hence will have a significantly reduced area, and use at least an order of magnitude less power, enabling a variety of network architectures and applications. The system will use very low power pulses for communication, well below what is required by health guidelines and exposure limitations. Distributing such small and smart sensors over the human body will allow users to monitor many vital signals, such as body temperature and electrocardiogram with unprecedented accuracy.
BAS/1/1686-01-01
Electrical Engineering ​
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Conceptual design of membrane processes
Academic Program: Chemistry
The aim of this project is to simulate different membrane processes to develop rules of thumb for conceptual design of membrane processes. The student in this project will learn how to use the software Aspen Plus®, a wide used software in petrochemical industry, to simulate the membrane process for the separation of propylene and propane. Through this project, the student will learn all the basic knowledge of membrane processes, understand the reason why gas separation is so important in chemical industry and what are the advantages and challenges in the membrane studies. The education aim of the project is to stimulate the interest of the student in membrane studies.
BAS/1/1375-01-01
test@gmail.com
x
​Chemical Engineering
Please contact professor for details.
Chemistry
Physical Sciences and Engineering
Graduate
Advanced Membranes and Porous Materials Center
Statistical models based on stochastic partial differential equations
Academic Program: Statistics
The student will learn about modern statistical methods based on stochastic partial differential equations (SPDEs). One important advantage with formulating statistical models using SPDEs is that it facilitates non-Gaussian extensions of several popular Gaussian models. Such extensions are useful for applications where the data has features that cannot be captured by Gaussian models. The goal of the project is to implement and compare these models for applications to longitudinal medical data and spatial environmental data.
BAS/1/1687-01-01
​Statistics
​​Written report and computer code that reproduces the results
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Online Outlier Detection for Functional Data
Academic Program: Statistics
The student will learn state-of-the-art statistical methods for functional data analysis and spatial statistics, explore various approaches of functional data ranking and outlier detection, especially for images and surfaces, develop algorithms for online outlier detection with applications to spatial deformation detection and brain signal analysis.​
WBS BAS/1/1655-01-01
​Statistics, Data Science, Environmental Science, Computer Science, Applied Mathematics
​At the end of the internship, the student is expected to implement the designed application and submit a written report or a poster​
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Internship on Deep learning Methods for Satellite Data Downscaling
Academic Program: Statistics
The student will learn state-of-the-art statistical and machine learning methods for image super-resolution, explore various sources of satelliteimages and deploy experiments to manipulate and analyze large scale dataset.​
WBS BAS/1/1655-01-01
​Statistics, Data Science, Environmental Science, Computer Science,Applied Mathematics​
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Continual Learning
Academic Program: Computer Science
Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot accessdatafrom previous tasks and when the model has a fixed capacity. In this project, the goal is to develop and improve the capability of the machine learning methods not to forget older concepts as time passes.References[1] Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach,MohamedElhoseiny, Efficient Lifelong Learning with A-GEM, ICLR, 2019[2] Mohamed Elhoseiny,Francesca Babiloni, Rahaf Aljundi, ManoharPaluri,Marcus Rohrbach, Tinne Tuytelaars, Exploring the Challenges towards Lifelong Fact Learning, ACCV 2018https://arxiv.org/abs/1711.09601[3] Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, Tinne Tuytelaars, Memory Aware Synapses: Learningwhat(not) to forget, ECCV 2018https://arxiv.org/abs/1711.09601[4]Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, MarcusRohrbachUncertainty-guided Continual Learning with Bayesian Neural Networks https://arxiv.org/abs/1906.02425For more references, you may visit https://nips.cc/Conferences/2018/Schedule?showEvent=10910 https://icml.cc/Conferences/2019/Schedule?showEvent=3528​​​
BAS/1/1685-01-01
​Computer Vision and Machine Learning
​Develop a working research prototype for a continual learning approach1) students should learn about machine learning, deep learning, and the respective target application chosen for the internship. 2) students are expected to show capability to go from an idea to a working prototype; pushing the limits of what the state of the art can do.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Imagination Inspired Vision
Academic Program: Computer Science
Imagination has been the source of novel ideas that enable humanity to progress at an ever-faster rate. It is also is one of the key properties of human intelligence that enables us to generate creative products like music and art. Imagination is not only helpful for creating Art and Fashion, it also helps us see the world. The goal of this project is to focus on developing techniques that empower AI machines to see the world (computer vision)orto create novel products (e.g., fashion and art), hence the name Imagination Inspired Vision.Fore more context; see my talk on this topic, recorded at the University of Southern California; https://bluejeans.com/playback/s/jC9ksXIMhbHTw25sLghFgeGoWGNHcM 9VYc6QJQ8Z0yEq9X5aogZUWJ0AwDKz8hSR Vision-CAIR group at KAUST stands for Computer Vision- "C" Artificial Intelligence Research. "C" is for Content or Creative since we cover in our lab both Vision Content AI research and Vision- Creative AI research. For more information, please visit:http://www.mohamed-elhoseiny.com/vision-cair-group​​​​
BAS/1/1685-01-01
​Computer Vision and Machine Learning
​Develop imagination inspired solutions machine learning research for helping understanding and the creation of the unseen. 1) students should learn about machine learning, deep learning, andtherespective target application chosen for the internship. 2) students are expected to show capability to go from an idea to a working prototype; pushing the limits of what the state of the art can do.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Diel Variations in the primary productivity of the upper ocean from autonomous glider and autonomous profiling float observations
Academic Program: Marine Science
Using autonomous gliders and profiling floats, we have investigated the interaction of physical and biogeochemical processes in the upper layers of the Red Sea. In all of these, we see distinct diel patterns of variability inkeybiogeochemical variables that include chlorophyll, suspended particles (particulate carbon), and dissolved oxygen. These processes represent the complex interaction of the daily solar cycle, nutrient supply, and physical processes that contribute to horizontal transport and vertical mixing of the water column. The goal of this study is to understand the diel variations in the key biogeochemical variables and determine how they are informing us about the primary productivity of the upper layer. The visiting student will work with existing data sets that we have been obtaining since 2013, and will also be able to participate in ongoing work that is currently ongoing within the Red Sea.​​​
BAS/1/1032-01-01
​Biological Oceanography, Primary Productivity
​1. Understand the presence and persistence of diel variability inkey biogeochemical variables that have been obtained with autonomous platforms in the central and northern Red Sea.2.   Evaluate whether it is possible from these data sets to determine ifnetprimary production is occurring, or if the system is in a static, recycling state. 3. If the outcome of this effort is deemed successful, and meaningful, we expect to include this work within a publication in which the student can participate​.
Marine Science
Biological and Environmental Sciences and Engineering
Red Sea Research Center
Microfluidics-based single-molecule fluorescence imaging of nanoscopic cellular interactions
Academic Program: BioScience
The adhesion of cells to the endothelium occurs through spatio-temporally regulated interactions that are mediated by multiple intra- and inter-cellular components. The mechanism of cell adhesion has been investigated primarily using ensemble-based experiments, which provides indirect information about how individual molecules work in such a complex system. In this project, we develop microfluidics-based in-vitro live-cell single-molecule fluorescence imaging technique to unravel nanoscopic spatiotemporal interaction between adhesion molecules during the cell migration. Specifically, we aim to address some key questions in the initial step of hematopoietic stem cell homing mediated by selectin-ligand interactions.
BAS/1/1028-01-01
satoshi.habuchi@kaust.edu.sa
Bioscience
​Fluorescence microscopy, Micro/nano fabrication, Optics, Biophysics, Immunology
​Development of new microfluidics-based live-cell single-molecule fluorescence imaging technique.Characterization of nanoscopic spatiotemporal interaction between selectins and their ligands occurring during the cell migration.​
BioScience
Biological and Environmental Sciences and Engineering
Development of shortwave infrared emitting fluorophores and bioimaging application
Academic Program: BioScience
One of the main challenges in applying fluorescence microscopy to large biological specimens including tissues and whole organisms, which is the latest focus in fluorescence imaging field, is the absorption and scattering of both excitation light and fluorescence emission by the specimens. Oneofthe best ways to capture high quality images through thick biological specimens is to use shortwave infrared (SWIR, 1,000-1,700 nm) fluorescence since both absorption and scattering of light are highly suppressed in this wavelength region. However, fluorophores that are available for SWIR fluorescence imaging at the moment are very limited. In this project, we develop new SWIR emitting fluorophores using conjugated polymer materials. Through in depth photophysical characterization including single-molecule SWIR fluorescence microscopy, we aim to develop new SWIR-emitting conjugated polymer nanoparticles. Based on the photophysical characteristics of the developed materials, we also aimtodevelop new SWIR fluorescence imaging techniques including time-gated imaging.
BAS/1/1028-01-01
​Fluorescence microscopy, Optics, Physics, Cell biology
​Development of new SWIR-emitting conjugated polymer nanoparticles. Detailed photophysical characterization of the developed materials.  Optimization of SWIR fluorescence microscopy system for bioimaging applications.​
BioScience
Biological and Environmental Sciences and Engineering
Impacts of UV radiation on corals and other organisms in the Red Sea
Academic Program: Marine Science
UV radiation has been identified as a key abiotic stressor in the marine environment, particularly in waters characterized by high optical transparency. One such environment is the Red Sea basin that, due to consistently minimal concentrations of UV-attenuating substances, has an exceptionally high transparency to UV radiation. This optical property and the proximity of the basin to the equator means that Red Sea organisms are exposed to intense, damaging levels of UV radiation.This project will investigate the effects of UV on scleractinian corals and other organisms in the Red Sea. ​
BAS/1/1072-01-01
​Marine Science
​Bibliographic search and bibliometric analysis.Design of field and laboratory experiments.Apply molecular laboratory techniques to evaluate UV effects on e.g. DNA damage, symbiosis state, lipid peroxidation, etc.Write final report summarizing the project work and key findings.
Marine Science
Biological and Environmental Sciences and Engineering
Red Sea Research Center
Biological stability of chlorinated and non-chlorinated drinking water
Academic Program: Environmental Science and Engineering
Drinking water is distributed from the treatment facility to consumers through extended man-made piping systems. The drinking water system should be microbiologically safe and biologically stable (WHO, 2006). The biological stability criterion refers to maintaining the microbial drinking water quality in time and distance from the point of drinking water production up to the point of consumption. This research will be conducted at the unique drinking water distribution system (DWDS) at KAUST a confined network of the same age supplied with reverse osmosis (RO) based drinking water. The aim of the project is to characterize temporal and spatial dynamics in biofilms and microbial community in the water from source to tap with the considerations of the impact of residual disinfectant use The results will allow better understanding whether residual chlorine is needed for distribution of RO produced drinking water and will lead to better insights on the biological stability of the produced water. ​
BAS/1/1024-01-01
​Chemistry, environmental science
Operate miniature drinking water distribution networks (preparation of solutions, setting up  and run equipment, problem solving) Sample analysis (biological and chemical analysis, DNA extraction, etc.Data analysis Written and oral presentation of (intermediate) results.​
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Characterization of biofilm growthrate in a membrane system
Academic Program: Environmental Science and Engineering
Membrane filtration plays an important role in seawater desalination and wastewater reclamation. Biofouling is an unacceptable loss of performance caused by the growth of bacteria inside the membrane element. The aim of the project is to establish relations between operational conditions, such as water velocity, production rate and nutrient concentration and the growth rate of biofilms. The results will allow better understanding and control of biofouling formation. The two main methods to observe the thickness of the biofilm are via its hydraulic resistance and via optical coherence tomography (OCT). ​
BAS/1/1024-01-01
​chemical engineering, environmental science, etc
​Formulate a research question and design the experiments accordingly.Run experiment (preparation of solutions, setting up and run equipment, problem solving, OCT-scan of biofilm)​Data analysisWritten and oral presentation of (intermediate) results.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Gradient compression for distributed training of machine learning models
Academic Program: Computer Science

Modern supervised machine learning models are trained using enormous amounts of data, and for this distributed computing systems are used. The training data is distributed across the memory of the nodes of the system, and in each step of the training process one needs to aggregate updates computed by all nodes using local data. This aggregation step requires communication of a large tensor, which is the bottleneck limiting the efficiency of the training method.

To mitigate this issue, various compression (e.g., sparsification/quantization/dithering) schemes were propose in the literature recently. However, many theoretical, system-level and practical questions remain to be open. In this project the intern will aim to advance the state of the art in some aspect of this field. As this is a fast moving field, details of the project will only be finalized together with the successful applicant. Background reading based on research on this topic done in my group:

https://arxiv.org/abs/1905.11261

https://arxiv.org/abs/1905.10988

 https://arxiv.org/abs/1903.06701

 https://arxiv.org/abs/1901.09437

 https://arxiv.org/abs/1901.09269

https://www.frontiersin.org/articles/10.3389/fams.2018.00062/abstract

https://arxiv.org/abs/1610.05492

https://arxiv.org/abs/1610.02527

BAS/1/1677-01-01
peter.richtarik@kaust.edu.sa
Computer Science
​computer science, mathematics, machine learning
​Ideally author or coauthor a research paper, and submit it to a premier conference in the field (e.g., ICML, AISTATS, NeurIPS, ICLR).​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Towards a Principled Understanding of Deep Learning
Academic Program: Computer Science

Deep learning models provide state of the art performance on many practical machine learning tasks. However, there is a large gap between our theoretical / conceptual understanding and practice.

The intern will work in one of the follow areas, depending on interest and background:

  • Deep learning models
  • Adversarial attacks and robustness
  • Optimization for deep learning
  • Generalization of deep learning
  • GANs
  • Model compression
BAS/1/1677-01-01
peter.richtarik@kaust.edu.sa
AI
​computer science, mathematics
​Ideally contribution to a research paper​.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Topics in Machine Learning and Optimization
Academic Program: Applied Mathematics and Computer Science
Topics in machine learning (ML). The project can be of a theoretical nature (e.g., design of optimization algorithms for training ML models; building foundations of deep learning; distributed, stochastic and nonconvex optimization), or of a practical nature (e.g., creative application and modification of existing techniques to problems in federated learning, computer vision, health, robotics, engineering). The precise topic will be decided together with the successful applicants, and will be tailored to their skills and background.
ASP/1/1669-01-01
​Computer Science, Mathematics or a related discipline
​Original research – contribution to a research paper​.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Mapping protein complexes in vivo
Academic Program: Plant Science
During multicellular development, the specification of distinct cell fates is often regulated by the same transcription factors operating differently in distinct cis-regulatory modules. In the model plant Arabidopsis root, distinct cell types are determined by the interacting transcription factors within one contiguous tissue layer. Using fluorescence lifetime microscopy we revealacell type-specific protein complexes that differentially regulate target genes and instruct distinct cell fates. In addition to determining the network function in other organs, we are currently studying how are these complexes established and maintained by generating a temporal interaction map and dissecting the transcriptional regulation by these complexes.We will use the following tools: FRET-FLIM, confocal microscopy imaging, mutants analysis.​
BAS/1/1081-01-01
​Developmental biology, cell biology
​Generate a spatio-temporal protein interaction map in vivo​
Plant Science
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
Molecular mechanisms underlying growth and defense in plants
Academic Program: Plant Science
When attacked by pathogens, plants allocate their energy and resources into defense responses at the expense of growth. Hence, understanding the mechanisms by which plants prioritize their responses is instrumental for improving plant defense and growth and consequently increasing crop yield. Recently, we have established a link between the defense hormone Jasmonic Acid (JA) and a transcription factor pathway with key roles in development. We have found that the developmental regulators BIRD proteins mediate Jasmonic acid defense response through interacting with core JA signaling pathway genes. The project aims to dissect the molecular framework underlying the dual function of these protein complexes. The identified signaling networks and pathways can then be rewired to allow simultaneous growth and defense. we will use the following tools: RNAseq, genome editing tools, fluorescence microscopy imaging amd a range methods for detecting protein-protein interactions in vitro and in vivo.
BAS/1/1081-01-01
​Developmental biology, cell biology
​The gained knowledge will be used to generate resistant crops with optimal growth behavior using genome editing tools.​
Plant Science
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
Spatio temporal analysis of expression of genes controlling assymetric stem cell division and tissue patterning in plants
Academic Program: Plant Science
BIRDs nuclear factors have been described to regulate root growth through association with the transcription factors SCARECROW and SHORTROOT, however their function in other organs remain to be elucidated. Here we propose to dissect network function in lateral roots and leaves. We will determine their physical associations spatially and during different developmental stages. We will also assess whether their target are regulated similarly.Objectives: In this project we aim to dissect how BIRD proteins regulate leave tissue patterning and map their localization in different mutant backgrounds. In addition, we will dissect binding sites in different target genes and alter specific binding by site directed mutagenesis. Technologies: confocal imaging microscopy, site directed mutagenesis, promoter activities using dual luciferase, plant phenotyping, cloning using gateway technology ​
ASP/1/1669-01-01
​Plant Biology​
​Map the expression of the genes at different developmental stages and dissect thein binding motifs
Plant Science
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
Experimental study of carbon-free combustion
Academic Program: Mechanical Engineering
A major goal of combustion research is to reduce emissions and minimize the harmful impact of energy production and transportation on the environment. Advancements in combustion sciences enabled strong reduction of NOX, SOX and particulates. The challenge for the next decade is reduction of carbon dioxide. One strategy is to completely remove carbon from the fuel, using carbon-free hydrogen carrier such as ammonia. Combustion of ammonia is not well understood, and no detailed information on the flame of ammonia-air flames is available. In this project the student will perform 1D Raman measurements of temperature and major species in ammonia flames in collaboration with a postdoc or a Ph.D. student. ​​​​​​
BAS/1/1388-01-01
​Mechanical, Aerospace, Chemical Engineering or Applied Physics
​The student will learn the fundamental of laser spectroscopy, and will gain hands-on experience in the operation of ammonia burners, and advanced laser diagnostics. He will acquire and analyze unique experimental datasets, and advance the understanding of combustion of ammonia-air flames.  ​
Mechanical Engineering
Physical Sciences and Engineering
Clean Combustion Research Center
Thermal Evaporation of Perovskite Absorbers for Silicon Perovskite Tandem Solar Cells
Academic Program: Materials Science & Engineering
Perovskite/silicon tandem solar cells are very promising technology to achieve >30% power conversion efficiency target. Despite this technology features promising efficiency potential, several challenges need to be overcome prior to possible industrial application. A particular challenge relates to the fact thatto maximize the light coupling into the bottom cells, the tandems require so-called random-pyramid textured bottom cells (with a feature size of several micron, see Fig. 1a). Whereas flat substrates have enormous reflection losses (Fig. 1c), textured substrates enable improved light coupling into the silicon wafers. Textured silicon solar cells are the industrial state-of-the-art, and there is likely little incentive to develop processes on expensive mirror-polished wafers. However, due to the characteristics of textured surfaces, which present irregularities and low-wetting properties, conventional solution techniques cannot be used to deposit the perovskite or the extraction layers.From this, it can be readily understood that the top cell fabrication needs to rely on scalable and conformal deposition methods, with a high degree of uniformity and reproducibility.  For these reasons, we aim in this project to alter our current state-of-the-art solution-based PSC processing procedures as much as possible towards scalable vacuum-based techniques. In this projects, the candidate will perform an evaporation of perovskites precursors and the conversion of the layers will be performed either by co-evaporation or vacuum/solution hybrid based methods. At the end of the internship period, students will learn how to fabricate and optimize perovskite solar cells. The candidate will have a chance to experience the fabrication of fully textured silicon/perovskite tandem solar cells.Why thermal evaporated perovskites?Scalability: vacuum based deposition techniques are a well-established technique in several industrial processes.Reproducibility: thermal evaporation process of perovskite absorbers is not affected by the multitude of uncontrollable parameters typical for solution-processed perovskites.Uniformity: unlikely solution deposition, evaporation enables uniform deposition, independently from the substrate type.Low thermal budget: thermal evaporation is a low-temperature process and the substrate can be eventually further cooled to room temperature.Environmentally benign: evaporation does not involve toxic solvents.Process flexibility: different perovskite composition can be obtained by evaporation, according to the desired application. ​
BAS/1/1387-01-01
​Solar Cells
​Thickness control of the evaporated lead iodideOptimization of co-evaporated of lead iodide and cesium bromide Process sophistication for triple cation system perovskites>23% tandem solar cell efficiency using thermal evaporated perovskites​
Materials Science & Engineering
Physical Sciences and Engineering
KAUST Solar Center
Broadband Transparent Front Electrodes for Perovskite Silicon Tandem Solar Cells
Academic Program: Materials Science & Engineering
This project is focusing on developing novel transparent electrodes for perovskite/silicon tandem solar cells whichare anemerging class of solar cells technology. Perovskite/silicon tandem solar cellsaim to achieve the power conversion efficiencies beyond the single junction limit of the silicon solar cells. Towards this end, developinghighly transparent electrodes are critical for the maximized light harvesting since parasitic absorption loses originating from the transparent conductive oxides causes drastic current losses. In this project, the deposition of the novel transparent conductive oxides will be performed the sputtering technique. The candidate will gain experience on the structural, optical and electrical and characterization of the thin films. Moreover, the candidate will have experience in the fabrication and characterization of the perovskite/silicon tandem solar cells to test the developed materials. Why we need broadband transparent electrodes on perovskite/silicon tandem solar cells?In perovskite/silicon tandem solar cells, the perovskite top cell efficiently harvests the blue part of the solar spectrum, while transmitting the red part, which is absorbed in the silicon bottom cell. In this way, the tandems can overcome the single-junction efficiency limit of silicon solar cells. However, parasitic absorption losses originating from the transparent electrodes hinders the light harvesting. Typically, a front transparent electrode in any solar cell has a sufficiently large (>3 eV) optical band gap to avoid absorption losses in the visible range of the spectrum. This requirement is already fulfilled by several TCOs such as ITO, IZO, and IO: H. For perovskite/silicon tandem solar cells, transparent electrodes with low absorptance in the NIR‐IR part of the spectrum are required since these devices use optical absorbers that are active in this range of the spectrum. Towards reaching >30% photoconversion efficiency, designing transparent electrodes with exceptionally large broadband transparency carriers critical importance. References[1] Morales‐Masis, M., De Wolf, S., Woods‐Robinson, R., Ager, J. W., & Ballif, C. (2017). Transparent electrodes for efficient optoelectronics. Advanced Electronic Materials, 3(5), 1600529.​
BAS/1/1387-01-01
​Solar Cells
​Optimization of the sputtering parameters of H:In2O3 and IZO transparent electrodesOptical and electrical characterization of the fabricated films Achieving <20 ohm/sq. sheet resistivity for 1000 nm thickness together with broadband transparency (<10% absorbance within this region).Exploring new TCO by metal doping of In2O3​
Materials Science & Engineering
Physical Sciences and Engineering
KAUST Solar Center
Novel Electron and Hole Transport Layers by Atomic Layer Deposition Technique for Perovskite Silicon Tandem Solar Cells
Academic Program: Materials Science & Engineering
The atomic layer deposition (ALD) technique is an efficient technique to deposit thin films at low temperature, which is based on self-limiting surface reactions by exposing sequentially on the substrate with various precursors and reactants. It provides excellent control over film thickness at the angstrom or monolayer level and deposition on high aspect ratio nano andmicrostructures with excellent step coverage. To exploit these advantages, we are employing ALD deposited electron and hole transport layers on randomly textured silicon wafers. In this project, to increase the light coupling in the tandem solar cells, the deposition of the broadband transparent electron and hole transport layers will be performed by the ALD technique. The candidate will gain experience on the structural, optical and electrical and characterization of the thin films by optimizing the deposition recipes. Moreover, the candidate will have experience in the fabrication and characterization of the perovskite solar cells and will have an opportunity to experience the fabrication of perovskite/silicon tandem solar cells. Why Silicon/Perovskite Tandems?The current global photovoltaics market (nowadays taken for more than 90% by crystalline silicon solar cells) has seen a sustained growth of its production capacity by more than 20% annually [1]. From a longer-term perspective, the market is expected to make a transition towards ultra-high efficiencies. For this, further efficiency improvement s will be needed, beyond the single-junction efficiency limit of silicon [2].  The most straight-forward way to do so is in the form of a silicon-based tandem solar cell, where a wider-bandgap top cell overlays the silicon bottom cell. In perovskite/silicon tandem solar cells, the perovskite top cell efficiently harvests the blue part of the solar spectrum, while transmitting the red part, which is absorbed in the silicon bottom cell. In this way, the tandems can overcome the single-junction efficiency limit of silicon solar cells. References[1] Haegel, Nancy M., et al. "Terawatt-scale photovoltaics: Trajectories and challenges." Science 356.6334 (2017): 141-143. [2] Richter, A., Hermle, M., & Glunz, S. W. (2013). "Reassessment of the limiting efficiency for crystalline silicon solar cells." IEEE Journal of Photovoltaics, 3(4), 1184-1191.​​​
BAS/1/1387-01-01
​Solar Cells
​Optimization of SnO2 thin films for perovskite solar cells applicationsTesting these layer for flat junction opaque devices for performance analysis Achieving >23% tandem solar cells efficiency using these buffer layers.Testing new ALD precursors for electron and hole transporting purposes
Materials Science & Engineering
Physical Sciences and Engineering
KAUST Solar Center
Salinity Tolerance of Plants
Academic Program: Plant Science
Soil salinity is a major abiotic stress constraining crop production. We are investigating how some plants are able to cope with salt stress, to then inform research on other crops to make them more tolerant to salinity stress. Quinoa (Chenopodium quinoa) tastes good, is highly nutritious and is a very salt tolerant crop; however, we are yet to discover the mechanisms for its high salt tolerance. This is one species that we are currently studying. We are also looking at mechanisms of tolerance in wild relatives of domesticated crops, in particular wild tomatoes and wild barley.​​
BAS/1/1038-01-01
mark.tester@kaust.edu.sa
Salinity
​Biology, Computer science
​Lots of good research / lots of hard work and fun.​
Plant Science
Biological and Environmental Sciences and Engineering
Undergraduate
Center for Desert Agriculture
Inverse Problems in Imaging
Academic Program: Computer Science
Inverse problems are abundant in the field of imaging, and range from simple image processing tasks such as denoising and deblurring to full-scale reconstruction problems like computed tomography (reconstructing 3D volumes from 2D projections). The purpose of this internship is to learn about inverse problems, and critical techniques for solving them, including convex and non-convex optimization, sparse coding, and compressive sensing.​
BAS/1/2902-01-01
​Computer Science, Applied Mathematics.​
​This project requires some familiarity with basic numerical methods as well as programming skills. Close collaboration with other team members is expected. Possibility for co-authoring a scientific article in a conference or journal.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Computational Cameras
Academic Program: Computer Science
Computational cameras are imaging systems that are combinations of optics, electronics, and algorithms that jointly enable new approaches to image sensing. Computational imaging systems are of interest for applications such as polarization imaging, hyperspectral cameras, time-of-flight and depth imaging, light fields, high speed cameras, or cameras with small and exotic form factors. The internship will be to learn about computational imaging approaches, and work in an interdisciplinary team to develop new camera systems in one of the application domains.​
BAS/1/2902-01-01
​Computer Science, Electrical or Optical Engineering
​Depending on the background of the student, the work can be more optics oriented or more software oriented. Close collaboration with other team members is expected. Possibility for co-authoring a scientific article in a conference or journal.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Fouling in Membrane Filtration Systems
Academic Program: Earth Science and Engineering
In the last decades the use of membrane systems for fresh water production has increased strongly to supply the growing water demand due to increasing human population, industrial and agricultural activity, economic growth and urbanization. Fouling represent one of the major drawbacks of membrane systems. In this project, we aim to explore different techniques in order to study the fouling developed in the system and relate with the membrane performance decrease. ​
ASP/1/1669-01-01
​Environmental science and engineering
​The student will learn various approaches to characterize the fouling developed in membrane filtration systems. He/She will learn different techniques (i.e. Confocal, Flow Cytometry, ATP, SEM, LCOCD etc). Several experiments will be run in order to relate the fouling developed in the system with the overall performance.
Earth Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Complex optoelectronics materials and phenomena
Academic Program: Electrical Engineering
Design, fabrication and characterization of new complex materials and phenomena for different optoelectronics applications​. ​​
BAS/1/1628-01-01
​Physics or Engineering or Chemistry
​Learning of different nanofabrication techniques; assembly of complex materials​ structures for different photonics applications
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Cutting-edge research on materials, devices, or physics of the third-generation semiconductor
Academic Program: Electrical Engineering
The wide bandgap semiconductors, also known as the third-generation semiconductors, have enormous potentials to revolutionize almost every industry on the planet and in space. Because of the unique and superior properties, they can be made in ultra-efficient, ultra-sensitive and ultra-reliable optical and electronic devices. Despite being in the early phrase of development and commercialization, the third-generation semiconductors have already resulted in industries worth of hundreds of billions USD and creating numerous jobs, as well as 2014 Nobel Prize in Physics. Opening your phone, computers and anything using electricity, you will likely find plenty of them. More potentials are to be unlocked by smart and hard-working researchers such as you. This VSRP project is offered by the Advanced Semiconductor Laboratory, a world-leading laboratory with state-of-the-art facilities in the third-generation semiconductor research. The VSRP students will have the opportunity to learn the latest materials, devices, and physics of the third-generation semiconductor as well as the combination with other exciting things such as 2D materials and quantum photonics. More importantly, every VSRP student will have his/her own project to solve a key problem. The project needs 4-6 months to be completed. It is going to be an exciting period with intense training and research work both theoretically and experimentally. Successful students can likely publish their results in prestigious scientific venues. ​ ​​​​​​​
BAS/1/1664-01-01
​Electrical/Electronic Engineering, Physics, Material Science, Chemistry
​In research, no deliverables can be compared with a patent or a peer-review paper. In the past, all the VSRP students can generate patents or publish first-authored or co-authored papers in prestigious journals. The publication record has help strengthened their credentials greatly for future career development. Therefore, the incoming VSRP students are expected to do the same. PS: one example can be found here: https://goo.gl/8sorGf​
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Plant-Beneficial Microbe Interaction
Academic Program: BioScience
Abiotic stresses are the most important factors for hampering plant growth and yield world-wide. However, beneficial microbes can help plants to enhance stress tolerance of plants. DARWIN 21 is a large scale project to isolate and study the interaction how rhizophere microbes contribute to enhance the capacity of plants under the most difficult abiotic stress conditions (http://www.darwin21.net/index.htm).In this project, the student will characterize several rhizosphere microbes and investigate whether they can confer plant resistance to different abiotic stresses. The student will learn how to apply techniques in microbiology, molecular biology and plant biology.​​
ASP/1/1669-01-01
​Microbiology, Genomics, Plant biology, Bioinformatics
​Isolation and characterization of bacterial strains. Analysis of beneficial microbes on plant physiology. Sequencing and bioinformatics to analyze microbial genomes, transcriptome and proteome analysis of beneficial microbes and plants.
BioScience
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
An iPSCs-based approach to model Type Two Diabetes in-vitro
Academic Program: BioScience
Studying the transcriptional and epigenetic mechanisms dysregulated in patients affected by metabolic disorders such as insulin resistance (IR) and type 2 diabetes mellitus (T2DM) is essential to derive efficient pharmacological approaches. We are seeking an outstanding student to work on a project focused on the study of the role of histone modifiers to the onset of metabolic disorders.​​​​​​
BAS/1/1077-01-01
​Molecular and Cellular Biology and/or Bioinformatics
​The selected candidate will use human stem cells and terminally differentiated glucose sensitive cell types and will acquire skills in molecular biology techniques including Chromatin Immuno-precipitation (ChIP), quantitative real-time PCR (Q-PCR) and next generation sequencing (NGS).​
BioScience
Biological and Environmental Sciences and Engineering
Screening for Carotenoid-Derived Signaling Molecules
Academic Program: BioScience
The project focuses on novel signaling molecules involved in plant development and response to environmental stress. It includes studies on the activity of selected carotenoid-metabolizing enzymes and the identification of their enzymatic products. Biological activity of products will be investigated by developmental assays using Arabidopsis and rice and by determining the effect of these compounds on the transcript levels of selected genes including strigolactone biosynthesis genes. These studies will be complemented by geno- and phenotyping of mutants disrupted in the corresponding genes. ​
ASP/1/1669-01-01
​Plant Biochemistry and Development
​Identification of new bioactive compounds/Better understanding of the regulation of strigolactone biosynthesis. Significant contribution to a publication 
BioScience
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
Machine Learning for Graphs
Academic Program: Computer Science

We have numerous projects where we work networks or graphs of various kinds, biological ones in particular. Networks can be undirected, directed with or without signs, discrete or continuous.

For publications see google scholar (https://scholar.google.com/citations hl=sv&user=_DUppAgAAAAJ&view_op=list_works&sortby=pubdate).

Challenges and sub-projects include:-         

How to compare 2 and several networks,review,benchmark current methods, invent new efficient algorithms for network comparison

-Analyze networks embedded in hyperbolicspace

-Review, benchmark current methods for embedding networks into anML framework

-Generative modeling of networks constrained by correlational information from data-sets

-Partially overlapping networks,analyzetheirputativealignment,constructionof multi-layer networks from several partially overlappinggraphs.

-Search and propagation in multi-layernetworks

-Alignment of several but different real protein interaction networks​

ASP/1/1669-01-01
jesper.tegner@kaust.edu.sa
LivingSystems
​Computer Science, Applied Mathematics
​Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. You will also get a good hands-on perspective at the frontier of machine intelligence and its applications in an interdisciplinary research group andenvironment.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Machine Learning for Biological and Medical Imaging
Academic Program: Computer Science

We have recently developed hybrid machine learning techniques for retinal images. For publications see google scholar(https://scholar.google.com/citations?hl=sv&user=_DUppAgAAAAJ&view_op=list_works&sortby=pubdate). Challenges include limited number of images, unbalanced data-sets, and interpretability of feature representations. Subprojects include to

Formulation and training of robust generative models (e.g.GANsand versions thereof) for the Retinal Dataset-

Extend and apply the techniques to melanoma datasets Develop and apply techniques to identify meaningful (biological/medical) feature representation from a successfulclassification

ASP/1/1669-01-01
jesper
bio
​Computer Science, Applied Mathematics
​Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. We expect you(a)tobringenthusiasm,creativity,andhardwork,(b)givelabseminarsonyourwork, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. You will also get a good hands-on perspective at the frontier of machine intelligence and its applications in an interdisciplinary research group andenvironment​.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate
Algorithmic Information Theory for Machine Intelligence
Academic Program: Computer Science
We recently developed numerical and computational techniques to use algorithmic information theory (AIT) to the analysis of networks. For publications see google scholar(https://scholar.google.com/citations?hl=sv&user=_DUppAgAAAAJ&view_op=list_works&sortby=pubdate).Subprojects include to-         Develop python packages for AIT analysis of large-scalenetworks-         Develop new AIT network embedding algorithms-         Analyze Convolutional Networks a representational learning usingAIT-         Quantify and benchmark AIT network analysis with othertechniques-         Large-scale computation of AIT networks using a supercomputer(Shaheen)Newand improved numerical approximation of algorithmic complexity using massive computations of Turing Machines on Shaheen(supercomputer)
ASP/1/1669-01-01
​Computer Science, Applied Mathematics
​Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report. In return this facilitates your critical thinking, presentations skills, and scientific writing. Your research, in collaboration and with support of team members, may lead to scientific publications. You will also get a good hands-on perspective at the frontier of machine intelligence and its applications in an interdisciplinary research group and environment.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Learning Generative Causal Models from Sparse Temporal Observations during Cellular Reprogramming
Academic Program: BioScience
​Recent work on stem cells and different mature specialized cells in different systems/organs (neurons, blood cells,) has revealed a stunning plasticity and capacity of reprogramming cells. For example, mature cells can be reprogrammed into pluripotent stem cells, and exciting work on engineered design of tissues and organs (organoids) are underway. On the one hand the community has since the sequencing of the human genome produced very efficient tools to read off the corresponding molecular events accompanying reprogramming and engineering of cells. Recently, the discovery of the CRISPR techniques has equipped us with unprecedented opportunities for precise writing or editing of the genomes. These developments in fundamental biology and biotechnology are currently opening new tools and perspectives of vital significance for drug development, regenerative medicine, synthetic biology, and personalized medicine. Yet, in essence all these efforts require and would be greatly facilitated if we could advance from correlative data-analysis to a predictive discovery of which interventions (edits, engineering) are producing which effects. Thus, we are facing the fundamental problem on how to discover causal relations from data, or in other words, can we derive quantitative predictive laws fromdata?We offer internships forseveral highly motivatedbachelor (B.Sc.) ormaster (M.Sc.) students who will explore this fundamental question primarily from a computational standpoint. This includes using high-performance simulations of dynamical models, and design of algorithms in a controlled in-silico environment. For example, to identify (a) efficient algorithms for generation of ensembles of dynamical models, (b) use supervised deep learning algorithms for pattern discovery in large-scale simulation data-sets, (c) to perform deep data-driven analysis of computational models in biology, (d) pursue investigations of transfer entropy and related techniques for system identification. These tools will be tested utilizing rich and recent molecular data on cellular reprogramming.​​ ​​​​
BAS/1/1078-01-01
​computer science, mathematical modeling, machine learning, systems biology, bioscience
​Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. The project is suitable for candidates fascinated by dynamical causal systems, be it computational or those we find in the natural world, i.e. living cells. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. We publish avidly in both bioscience and computational sciences, not for the fame but rather as steps aiming to and motivated both by our quest of asking fundamental questions of relevance to human nature and discovery of transformative intelligent technologies inspired from nature. You will get a good hands-on perspective on the frontiers in dynamical systems and bioscience using state-of-the-art simulation and machine learning tools.
BioScience
Biological and Environmental Sciences and Engineering
Deep Learning and Machine Intelligence for Single Cell Genomics
Academic Program: BioScience
Single cell biology and genomics in particular are currently transforming the biosciences. Single cell RNA sequencing (scRNAseq), method of the year 2013 (Nature Methods), has now matured and large amounts of scRNAseq are now available. These data, characterizing living systems at an unprecedented level of resolution, hold the promise to set the stage for a fundamental quantitative understanding of living systems with special reference to genomic regulation and collective computation. Yet, there are a number of open problems on how to think about these data and how to pragmatically analyze them.In parallel, we have witnessed a rapid development in machine learning. The rise of computation, such as supercomputers (shaheen@KAUST) and GPU based techniques, in conjunction with data explosion (often referred to as big data), has fuelled the development of new techniques aiming for machine intelligence. In particular, techniques inspired from livings systems, such as deep convolutional networks, currently experience a renaissance. Driving forces include not only data and computation but also the availability of suite of open source platforms (e.g. Theano, Caffe, Torch7, TensorFlow) supporting machine-learning algorithms. These algorithms represent industry standard for processing images, speech, text, and runs on the majority of services and devices provided by Google, Amazon, Facebook, to name a few big players, as well as a numerous startups.We offer internships for several highly motivated bachelor (B.Sc.) or master (M.Sc.) students who will identify (a) appropriate supervised deep learning architectures and training algorithms for scRNAseq data, (b) explore generative adversarial network (GANs) techniques for estimation of high-dimensional data distribution in the single cell gene expression space. This work will be used to develop new techniques and to address open problems in single cell genomics such as pseudo-temporal ordering of single cell data, clustering of data, investigate representations, transfer learning, and unsupervised feature discovery. ​​​
BAS/1/1078-01-01
​computer science, bioscience, machine learning, systems biology, artificial intelligence​
​Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. The project is suitable for candidates fascinated of living systems, interested in cutting edge bioscience, and artificial intelligence for science and not for discovering cats in YouTube. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. We publish avidly in both bioscience and computational sciences, not for the fame but rather as steps aiming to and motivated both by our quest of asking fundamental questions of relevance to human nature and discovery of transformative intelligent technologies inspired from nature. You will also get a good hands-on perspective at the frontier of bioscience and machine intelligence in an interdisciplinary research group and environment.
BioScience
Biological and Environmental Sciences and Engineering
Brain Inspired Computing
Academic Program: Electrical Engineering
Diagnostics become more important in third world countries as the people have limited access to medical care systems and have less awareness of healthy lifestyles. There is certainly a need for on-site detection in the life science fields; and for point-of-care diagnostics in rural areas of underdeveloped countries so that even an unskilled person can use the device to determine the presence of disease-causing markers. Currently, diagnostics commonly employ long assay time, trained personnel, sophisticated instruments, and require financial support. A good approach to overcome this current situation would be the use of flexible and paper-based point-of-care devices to detect specific biomarkers. Biomarkers provide insight into normal biological processes, pathogenic processes, and pharmacological therapeutic interventions. Hence, the development of more compatible, reliable, convenient, simple, easyto- use systems would be of great use to a person less skilled in medical diagnostic procedures.​​​​​
BAS/1/1605-01-01
​Electrical Engineering, Computer science, physics, neurosciences
​1-      A complete biosensor design and simulation  2-      Potential fully operational Hardware device  3-      Full detailed report on the design and participation in manuscript and papers writeup​
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Biomedical Sensors
Academic Program: Electrical Engineering
Conventional computing based on Von Neumann architecture has been shown to be approaching its limits in scalability and power consumption. If solved with contemporary machines, today’s applications in science and industry related to data analysis, pattern recognition and prediction would demand a huge computing power. In the era of ubiquitous sensing and data acquisition, a way to cheaply and power efficiently make sense of the collected ‘big data’ is of utmost importance. Here, human brain’s efficiency becomes the ultimate standard and inspiration for any future technology. Such trend of understanding the brain behavior is currently gaining a huge attention worldwide. At the sensors lab, students under the supervision of Prof. K.N. Salama are exploring new computing technologies miming the way our brains process and store data.​​​​
BAS/1/1605-01-01
khaled.salama@kaust.edu.sa
sensor
​​Electrical Engineering, Computer science, physics, neurosciences
 Report of state of art brain inspired computers;   Implementation of state of art; Exploration of neuromorphic architectures;  Simulation and comparison of various alternatives​. ​
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Development of a novel Stimulated Raman Scattering microscopy system
Academic Program: BioScience
Microscopy techniques based on vibrational spectroscopy are poised to be part of the next generation of microscopes for biological applications based on their unique chemical contrast and sub-cellular resolution for non-invasive, non-destructive and label free imaging of biological samples as live cells. The project will focus on the development of a fast and low-noise detection system in a setup for microscopic vibrational spectroscopy based on Stimulated Raman Scattering, which is one of the most advanced and sensitive methods for label-free microscopy for bio-imaging. The system will be applied to vibrational imaging of cancer stem cells to unveil their specific bio-chemical signatures. ​ ​​​​
ASP/1/1669-01-01
​Electrical Engineering, physics
​Learn Coherent Raman Scattering techniques. Design, assemble and test circuitry for multiplexed and low-noise detection in a Stimulated Raman Scattering microscopy setup based on femtosecond broadband laser sources. Demonstrate fast and high S/N ratio imaging with multiplex (broadband) Stimulated Raman Scattering microscopy. ​ 
BioScience
Biological and Environmental Sciences and Engineering
Novel Micro-optical structures on optical fiber tip with two-photon lithography
Academic Program: BioScience
Optical fibers are nowadays an ubiquitous core element of telecommunication systems, new laser technologies and biomedical devices. Manufacturing techniques for optical fibers have been developed and refined in order create manifold geometries and optical properties (e.g.Dual clad fibers, fiber bundles, Photonic Crystal Fibers, to name a few). Yet the capability to fabricate complex miniaturized structures integrated with optical fibers to realize important optical functions (like beam shaping, beam deflection, fiber optical tweezers, etc.) has been demonstrated only very recently.The project will focus on the fabrication of optical wave-guiding structures on the tip of optical fibers exploiting to flexibility, resolution and 3D fabrication capability of Laser Direct Writing based on Two-Photon Lithography (TPL).​ ​​​​
ASP/1/1669-01-01
​Electrical Engineering, physics
​Learn Two-Photon Lithography. Design structures using wave-optics propagation software. Fabricate structures on optical fibers. Measurements to assess optical function of fabricated structures.​
BioScience
Biological and Environmental Sciences and Engineering
Wireless Networks for Better Smart Buildings
Academic Program: Computer Science
Smart building involves a construction of multiple layer system that is capable to sense, acquire, analyze and perform the proper action to maintain the operation of certain city entity. The layered system incorporates six layers including: sensing layer, network layer, software layer, semantic discovery layer, processing and reasoning layer, and service layer. All the statistics show that there will be a large demand for smart buildings in the future as it has the potential for energy saving, indoor services such as tracking, mapping, localization, identifying products, and downloading relevant information are the main drive for future trends. The number of smart devices is expected to be x10 of the current number in 2020. Efficient Resource allocation for context aware based systems with the future demand for smart devices is challenging. Thus, it is essential to utilize techniques such as data mining and machine learning for data analytics, reasoning, and decision-making in order to maximize the smart building operation efficiency and overcome all the related challenges.​​
BAS/1/1601-01-01
​Computer Science
​The student will consider smart building services as a core for gathering data. Such data can be collected and transmitted in several ways. However, we will be interested in wireless technologies as a platform using simple devices such as smart phones. We gather the data and use data mining and machine learning techniques to make the right decisions such that we guarantee the sustainability of the smart building services.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Realtime transmission in Underwater Wireless Environments
Academic Program: Computer Science
Design and implement a system that facilitates realtime data transmission in underwater wireless networks. This includes examining the data transmission over several types of water conditions along with extracting the data transmission characteristics.  ​​
BAS/1/1601-01-01
​Computer science and/or electrical engineering
​The student is expected to closely work on an underwater wireless optical transmission testbed. Using the testbed equipment’s, the student will be running several sets of experiments and collect certain data (e.g., bit error rates, frame rates, channel condition, ..etc). The extracted data will be later used to design an optimal resource allocation framework for facilitating realtime transmission in such environments. ​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Wireless Sensor Node Analysis Employing Energy Harvesting
Academic Program: Computer Science
The project consist of a theoretical and a system research thrusts in energy-harvesting wireless sensor networks. Looking at the energy harvesting module in sensor node from the system’s prospective, we observe that adopting an interruption/harvesting policy enhances the energy consumption, but it also increases average packet end-to-end delay and packet dropping! There are various tradeoffs exist in wireless sensor network (WSN) design. Of particular interest to the project are 1) End-to-end delay vs. energy harvesting & network size, and 2) Increasing network size allows for a smaller number of data sink nodes and reduces dropping but it also increases average end-to-end delay. In order to quantify the tradeoffs, we raise a question. How delay, network size, and harvesting policy (service vacation) interact with each other? In other words, how large the network dimensions can go to considering certain packet latency threshold and dropping? Energy harvesting module can arbitrary be triggered, upon empty buffers, and thus, imposes random interruption periods on the sensor node, these random cycles (vacations) increases latency due to residual vacation time that is consumed for harvesting. In order to solve the above challenge, the student will study this system and think of a theoretical and a system approaches (with the assistance of the instructor) to improve the end-to-end delay. For instance, a possible solution would be, instead of arbitrary harvesting time, we aim at optimizing this value to minimize the delay and dropping. We also aim at adding a constraint that forces the sensor node to trigger the harvesting phase once its battery is low! Currently, the networking lab has a set of 20 sensor motes that are programmable using TinyOS and also has all the necessary mathematical packages to evaluate the student proposed models. ​​​
BAS/1/1601-01-01
​Computer science and electrical engineering
​The student is expected to achieve:1.      (theatrical component): An optimization of the energy harvesting value that minimizes the system end-to-end delay validated using matlab. 2.      (systems component): Translate the results from (1) into a working code, which can be tested over our TelosB sensor motes using TinyOS (C/C++).   The above results, if completed, are considered novel and can result into a publication with the agreement with the instructor.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
A domain- specific language and code generation for Riemann solvers
Academic Program: Applied Mathematics and Computer Science
Riemann solvers are the core algorithms in numerical methods for wave propagation. They are also the most complex part of developing code for new applications. The design and implementation of an effective approximate Riemann solver typically requires substantial expertise and time. However, there exist generic approaches that utilize relatively straightforward properties of the hyperbolic system and are reasonably efficient. The goal of this project is to develop a domain- specific language for hyperbolic conservation laws and Riemann solvers, and implement automatic generation of efficient solvers based on a purely symbolic representation of the problem.  ​​​​​
BAS/1/1616-01-01
​Mathematics, computer science or engineering
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Laser-based Sensor Design and Development
Academic Program: Mechanical Engineering
The project involves design, development, and implementation of a laser-based optical sensor. The sensor will be used to monitor environmental pollutants (e.g., NOx, CO) or greenhouse gas emissions (e.g., N2O, CH4, CO2). The student will work on studying the electromagnetic spectrum of various molecules, choosing the candidate optical transitions, setting up the laser-based optical setup, and performinglaboratory measurements to detect species concentration in trace quantities. Advanced sensing strategies, such as wavelength modulation, cavity-enhancement and frequency combs will be utilized. The student will gain expertise in spectroscopy,statistical thermodynamics, machine learning, optical engineering, and mechanical design.​​​
BAS/1/1300-01-01
​Physics, Electrical Engineering, Mechanical Engineering, Chemical Engineering
​Spectral line selection using state-of-the-art spectroscopicmodels Design of optical, electronic and mechanical components of thesensor Laboratory validations of the sensorperformance ​Field testing for trace gasdetection​
Mechanical Engineering
Physical Sciences and Engineering
Clean Combustion Research Center
Future Fuel for Advanced Combustion Engines
Academic Program: Chemistry
Increasing focus on global warming and CO2 emsisions is pushing the engine technology to new frontiers. The overarching objective is to increase the efficiency of internal combustion engines so as to minimize the CO2 emissions. Advanced engine technologies revolve around compression ignition concepts but diesel is not deemed to be suitable fuel for achieving higher efficiency and low pollutant levels. In this context, we are exploring new fuel formulations which can be produced at a lower cost from the refinery but can provide superior performance in the engine. The project will involve exploring the ignition and emission characteristics of such candidate future fuels.​​​​​
BAS/1/1300-01-01
​Mechanical Engineering, Chemical Engineering, Chemistry
​Perofrm detailed physical/chemical characterization on select refinery stream fuels;Carry out ignition experiments in idealized reactor configurations of shock tube and rapid compression machine; Perform chemical kinetic modelling to develop surrogates for the fuels; Analyze the performance of new fuel in engine simulations; Recommend the optimal fuel formulation suitable for advanced compression ignition engines​.
Chemistry
Physical Sciences and Engineering
Clean Combustion Research Center
Chemical Kinetics of Novel Biofuels
Academic Program: Mechanical Engineering
Biofuels are becoming increasingly important in the world's energy infrastructure and their use has steadily been increasing in Europe and the U.S. With a number of candidate biofuels, a concentrated effort must be spent on choosing the optimal fuel that can be produced in a cost-effective manner and deliver the best performance within the engine. The student will initially work on choosing a few candidate biofuel molecules for investigating experimentally in the laboratory. Thereafter, he/she will perform a series of experimental studies on the selected fuels to understand their chemical kinetics behavior.​​​​​​​​​
BAS/1/1300-01-01
Mechanical Engineering or Chemical Engineering
- Perform life-cycle-analysis to select candidate biofuels - Characterize various biofuel molecules in terms of their physical properties - Conduct detailed experiments on the chemical kinetics behavior of these biofuels using shock tube and rapid compression machine - Run validated chemical kinetics models to estimate the performance of studied biofuels in an engine - Show how the new fuel leads to improvement in engine efficiency and reduction in emissions ​​​​
Mechanical Engineering
Physical Sciences and Engineering
Clean Combustion Research Center
Theoretical and numerical study on complex materials
Academic Program: Applied Mathematics and Computer Science
​The students are required to perform theoretical and numerical studies on wave propagation in artificial structures with complex structures. The contents include but are not limited to Fano resonance, absorption, trapping of electromagnetic or acoustic waves. ​​​​​
BAS/1/1626-01-01
​Physics, Mathematics, Material Sciences, and related
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Nonlinear Partial Differential Models
Academic Program: Applied Mathematics and Computer Science
In this project we will study some nonlinear partial differential equations models that arise in applications ranging from population dynamics, mean-field games, quantum chemistry and mechanics, medicine, quasi-geostrophic flows, and water waves.​​​​​
11111.0000000000
Mathematics or related field
The objective of the project is to study in detail modeling, analytical and numerical aspects of partial differential equations from concrete applications. A final report and presentation will be required. The work will be developed and the guidance of Prof. Diogo Gomes and the Research Scientist Dr. Saber Trabelsi.​
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Classification of long non-coding RNAs
Academic Program: Computer Science
​Long non-coding RNAs (lncRNAs) have been found to perform various functions in a wide variety of important biological processes. To make easier interpretation of lncRNA functions and conduct deep mining on these transcribed sequences, it is important to classify lncRNAs into different groups. lncRNA classification attracts much attention recently. The main technical difficulties are 1) the limited number of known lncRNAs (small training sample size), and 2) the very different lengths of lncRNAs. This project is to apply and further improve the string kernel algorithms developed in Prof. Gao’s group to the lncRNA classification problem. ​​​​​​​
BAS/1/1624-01-01
​Computer science, bioinformatics, electrical engineering, applied mathematics​
The visiting student for this project is expected to finish the following deliverables:1.      Give a throughout literature review on lncRNA classification methods and potential machine learning methods that can be applied to this problem. 2.      Get familiar with the string kernel algorithms developed in Prof. Gao’s group. 3.      Gather an lncRNA dataset to be used as the benchmark set for this research. 4.      Conduct a comprehensive comparative study of the state-of-the-art methods on the benchmark set. 5.      Apply the string kernel algorithms on lncRNA classification and evaluate the performance. 6.      If necessary, improve the string kernel algorithms to achieve better performance.Write a report to summarize the results.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Computational Bioscience Research Center
Novel Pincer complexes for catalysis
Academic Program: Chemistry
The student will work on the design and synthesis of 2-amino-pyridine based pincer ligands (3-10 steps) and the preparation of their corresponding transition metal complexes. Students will trained on standard organic synthesis knowledge and Schlenk skills.  For more information see:https://pubs.acs.org/doi/10.1021/acscatal.8b04495​
BAS/1/1334-01-01
Kuo-wei Huang
Chemistry
​Catalysis and Organometallics​
​Preparation and characterization of new complexes. Development of catalytic applications​ 
Chemistry
Physical Sciences and Engineering
KAUST Catalysis Center
Role of non-classical hydrogen bonding in organocatalysis
Academic Program: Chemistry
The student will utilize kinetic (NMR, IR, etc) and computational tools (DFT calculations) to elucidate the role of hydrogen bonding network and in particular the non-classical hydrogen bonding in the thiourea and guanidine-based organocatalysis.  ​​​​​
BAS/1/1334-01-01
​Chemistry
​Assist in the kinetic study and DFT calculations.​ 
Chemistry
Physical Sciences and Engineering
KAUST Catalysis Center
Deep Learning for Visual Computing
Academic Program: Computer Science
The internship will be in the area of visual computing (computer graphics, computer vision, remote sensing). The exact topic depends on the student's interest, student's background, and current research topics in thegroup. To give some examplesof past projects, our group worked on topics related to generative adversarial networks for synthesizing images, textures, point clouds, 3D geometry, ..., networks using graphs as representations, and 3D reconstruction problems such as depth from one or multiple images, primitive fitting, indoor room layout reconstruction and segmentation of images.​
BAS/1/1630-01-01
​Computer Graphics, Computer Vision, Deep Learning
​There are two learning objectives for the internships:1) students should learn about machine learning, deep learning, andtherespective target application chosen for the internship.​2) students should implement a working prototype
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Computer Graphics, Computer Vision, and Visualization
Academic Program: Computer Science
The internship is in the area of graphics, vision, or visualization. The exact topic is determined in discussion with the student to obtain a good fit with the student’s interest and background. Example projects are 3d reconstruction from images and laser scans, geo-spatial visualization, remeshing, sampling, procedural modeling, and design computation using machine learning. ​​​​​​
11111.0000000000
​Computer Science
​The student should either contribute to an existing research project or leading his/her own project. That includes reading literature, solving technical problems, and implementation.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center