Thank you for your interest in applying to VSRP internships at KAUST.

Please note that currently there are entry restrictions in Saudi Arabia for travelers from certain countries, due to the pandemic. If you are an approved applicant, our office will update you when we can bring you safely to campus. For new applicants, we will continue reviewing applications and processing them.

If you are interested in a Remote VSRP internship, please check our full list of faculty who are supporting Remote interns here and apply to the project of your choice.

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Total Result(s) Found: 214

Concentrated Solar Thermal Energy for Mineral Processing

Academic Program: Mechanical Engineering

A Machine Learning Approach to Unentangle Wetting

Academic Program: Chemical Engineering

Academic Program: Environmental Science and Engineering

Liquid-repellent surfaces are utilized in a wide spectrum of practical applications, including anti-fogging coatings, reducing frictional drag, water desalination, separating alcohols and other volatile organics from fermenter broths, and beyond. The most common criteria for quantifying their liquid-repellence are based on measuring apparent contact angles – advancing and receding– of sessile droplets of probe liquids placed onto them. Of these, the receding contact angles provide crucial information about the chemical or topographical make-up of the surface. However, despite over two centuries of research on contact angles, there are no well-established theories for predicting receding contact angles, for instance, as a function of the surface micro/nano (hierarchical) texture and chemical make-up and the speed of the liquid front. Here, we propose to assess the efficacy of a Machine Learning approach towards predicting receding contact angles from surface roughness features. We aim to design and evaluate a neural network architecture for automatic inference of dynamic wetting properties including advancing and receding contact angles at various speeds, surface topography and chemical make-up, and drop size and volume. Subsequent analysis of model parameters can point to key surface features that contribute to changes in the output variable, which can help advance our fundamental understanding of wetting of complex surfaces, implicit in water science and technology.

BAS/1/1070-01-01

himanshu.mishra@kaust.edu.sa

machine learning, microstructures, wetting

machine learning, microstructures, wetting

The intern student will be tasked with: 1. Collecting a dataset of images of hierarchically textured natural surfaces and micro/nano fabricated surfaces, labeled with static and dynamic wetting properties and chemical composition. 2. Helping design and implement a neural network architecture, optimized for the prediction of apparent contact angles on hierarchical surfaces (e.g., mico, nano, or mili meter scale). Project-duration will be 3-6 month, and the student arrival/departure dates will be discussed.

Chemical Engineering

Biological and Environmental Sciences and Engineering

Water Desalination and Reuse Center

Electro-catalyzed C-C and C-X bond cross-couplings

Academic Program: Chemical Science

Establishment of the extremophile Cyanidioschyzon merolae for biotechnological applications

Academic Program: BioEngineering

Expediting surface wave dispersion curve picking with ML

Academic Program: Earth Science and Engineering

Machine Learning and Dynamical Systems

Academic Program: Applied Mathematics and Computer Science

Learning to Cooperate in Multi-Robot Systems

Academic Program: Electrical Engineering

How can we make a group of robots to cooperate in perception and manipulation to perform a variety of missions? Examples include a team of small robot helicopters and ground robots, equipped with manipulators (robotic arms), productively monitoring and harvesting crops in precision agriculture or proactively identifying and removing potential hazards in smart infrastructure applications. To realize this, the robots need to have the ability to organize as a team and to cooperate with their peers in team missions. The project aims at studying principles and algorithms to realize robots' ability to learn to cooperate. In particular, the main theme of the study focuses on how to generalize the robots' learning capability across diverse environments and even different application domains. To answer this question, we begin by studying reinforcement learning principles designed for multi-robot cooperation and systematically investigate how the robots' learning process depends on parameters defining the systems and environments. Then we develop new models and computational tools to train a team of robots that provide performance guarantees across a wide range of application scenarios.

BAS/1/1695-01-01

shinkyu.park@kaust.edu.sa

Robotics, Multi-Robot Learning, Multi-Robot Cooperation

Electrical and Computer Engineering

The goal of this project is to develop, implement, and test a multi-robot learning framework. The students will have opportunities to learn many of computational methods designed for multi-robot cooperation/learning and experience designing and implementing their own algorithms using multi-robot systems. The students are encouraged to work on research-oriented problems and expected to come up with their own creative solutions. They should have backgrounds in reinforcement learning and know fundamentals in multi-robot control/planning, and they are expected have experience with robotics software and proficiency in programming languages (C/C++ and Python).

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

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.

Biological and Environmental Sciences and Engineering

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

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.

Biological and Environmental Sciences and Engineering

Characterization of chemical contaminants in wastewater to predict its role on natural transformation among microorganisms

Academic Program: Environmental Science and Engineering

Robot Navigation in Crowded Environments

Academic Program: Electrical Engineering

One of big challenges in robotics research is in enabling robot navigation in crowded environments. As one key technical difficulty, in such environment, a robot would trap into a deadlock state where the robot is unable to advance to its destination, especially when it is surrounded by pedestrians and perceives them as obstacles which the robot is programmed to avoid collision against. This problem is widely known as a "freezing robot problem" and acts as a major hurdle in deploying robotic systems in real-world applications. We study this problem in the context of human-robot interaction and develop new algorithms that ensure deadlock-free robot navigation in pedestrian-populated environments. The project aims at developing both principles and algorithms to address the freezing robot problem: we begin by investigating how the human-robot interaction can be understood as a feedback interconnection of human and robot decision-making models and then identify under what specifications on the robot's decision-making model, the robot is guaranteed to be deadlock-free. We will explore tools from feedback control theory and game theory to find such specifications and develop robot navigation algorithms satisfying them. Ultimately, we will carrying out experiments using multiple mobile robot platforms to validate our framework in pedestrian-populated areas.

BAS/1/1695-01-01

shinkyu.park@kaust.edu.sa

Robotics, Human-Robot Interaction, Feedback Control

Electrical and Computer Engineering

The goal of this project is to develop, implement, and test a deadlock-free robot navigation framework based on well-established multi-agent decision-making models. The students will get to learn many of tools designed for robot perception and navigation tasks and gain experience in designing and implementing their own algorithms into robot platforms. Then the students are expected to design new methods to predict pedestrian motions using data from on-board sensors and to maneuver the robot in crowded areas. The students are encouraged to work on research-oriented problems and expected to come up with their own creative solutions. They should have backgrounds in robot perception and motion planning, and they are expected have experience with robotics software and proficiency in programming languages (C/C++ and Python).

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Numerical approximation of partial differential equations

Academic Program: Applied Mathematics and Computer Science

Imaging with a Drone-borne Synthetic Aperture Radar

Academic Program: Electrical Engineering

Causal and Fair Machine Learning

Academic Program: Computer Science

Robust/Differentially Private Machine Learning

Academic Program: Applied Mathematics and Computer Science

Foundations of Private and Fair Statistics

Academic Program: Statistics

Lithofacies classification with transition-aware Neural Networks

Academic Program: Environmental Science and Engineering

Hydrodynamic characterization of membrane spacers

Academic Program: Environmental Science and Engineering

Protocol development for biomass quantification in membrane autopsies

Academic Program: Environmental Science and Engineering

KAUST-BioMOFs

Academic Program: Chemical Science

Impact of Holocene climatic shifts on the spatial sediment distribution in a carbonate lagoon

Academic Program: Energy Resources and Petroleum Engineering

Influence of short-term climate changes on the spatial facies distribution ina carbonate platform lagoon during Holocene (N Red Sea)

Academic Program: Earth Science and Engineering

Numerical Study of CO2 Storage in a Deep Aquifer in Saudi Arabia

Academic Program: Energy Resources and Petroleum Engineering

Tectonic evolution through analogue modelling

Academic Program: Energy Resources and Petroleum Engineering

Large-eddy Simulation of boundary layer bypass transition

Academic Program: BioScience

High-resolution numerical modeling of atmospheric processes over the Arabian Peninsula

Academic Program: Earth Science and Engineering

Assessment of CCS in Saudi Arabia

Academic Program: Energy Resources and Petroleum Engineering

The aim of the project will be to contribute to the assessment of the CCUS potential in Saudi Arabia through the assessment of stationary CO2 emissions as well as the cost analysis (separation, capture, and transportation costs involved in CCS). The database for CO2 emissions from stationary sources in the Kingdom includes emissions from electricity generation, desalination, oil refineries, cement industry, petrochemicals, and iron & steel from 2016, that need to be verified and updated with most up-to-date data. The assessment will also include looking at identifying potential storage locations and estimating the capacity of storing CO2 in deep aquifers, depleted hydrocarbon reservoirs, and basaltic rocks.

The VSRP student will be involved in data analysis and interpretation in Petrel and, by picking up new ideas and techniques, will be able contribute to the further development of the Petrel/ArcGIS geological model of Saudi Arabia in order to identify and characterize potential CO2 storage sites in the country.
The student will also help in establishing and developing the national carbon storage atlas for KSA. A carbon storage atlas is a comprehensive assessment of all aspects of CCS including CO2 emissions, best practices, and emerging technologies related to carbon capture, transportation, and assessments of storage in potential geological sites and associated costs.

The work may include this section petrography using optical and electron microscopes to characterize rocks for carbon disposal.

BAS/1/1400-01-01

Alexandros Tasianas eamail alexandros.tasianas@kaust.edu.sa

CO2 storage, CCS, GIS, geology

Carbon Capture Utilization and Storage (CCUS)

Contribute to the construction, verification and use of the 3D geological model of the Saudi Arabia using GIS and specialized geological interpretation software (creation/modification of formation top surfaces corresponding to potential storage sites, input of data e.g. well information, etc)

Verification of input and update of the existing GHG emissions database with the most recent data for the current CO2 stationary sources with locations, rates and industry sector for the country.

Energy Resources and Petroleum Engineering

Physical Sciences and Engineering

Ali I. Al-Naimi Petroleum Engineering Research Center

Diversity and ecology of the coral genus Leptoseris in mesophotic environments

Academic Program: Marine Science

Diversity and biogeography of the organ pipe coral Tubipora

Academic Program: Marine Science

Body Area Networks

Academic Program: Electrical Engineering

Conceptual design of membrane processes

Academic Program: Chemical and Biological Engineering

Data and power conversion using Microelectromechanical systems (MEMS)

Academic Program: Electrical Engineering

Spintronics Memory, Logic Devices and Circuits for Neuromorphic Computing Systems

Academic Program: Electrical Engineering

Molecular doping of organic semiconductors for high efficiency optoelectronic devices

Academic Program: Materials Science & Engineering

Doping of organic semiconductors plays a fundamental role to overcome typical limitations observed in organic electronic devices. Organic light-emitting diodes (OLEDs) and organic solar cells (OSC) benefits for instance of the introduction of highly conducting injection layers, while high conductivity is one of the basic requirements for organic thermoelectric materials. Organic field-effect transistors (OFETs), on the other hand, are almost entirely based on intrinsic materials and doping has been mainly employed to pattern areas close to the contacts in order to improve charge injection.1 Beside the investigation of high degree of doping, ultralow doping has been recently attracting great interest, with the aim of directly dope the devices active layer that were usually based on intrinsic organic semiconductors. OSC containing small weight percentage of molecular dopants in the bulk heterojunction were found to display an increased short-circuit current (Jsc) and hence higher power conversion efficiency (PCE), when compared to their intrinsic counterpart.2 OLEDs have been reported with improved performance and device color modulation with dopant concentration was reported.3 A similar approach has been employed for OFETs, where extremely high mobilities, beyond the highest reported in literature, have been reported for organic semiconductors blended with low concentrations of dopants.4–6 These latter approaches are based on the addition of low fractions of dopants (£ 1-2 mol%), hence providing a different scenario from that of highly doped conducting layers. Strong efforts have been spent in the understanding of doping in organic semiconductors, both from a chemical and physical point of view, providing hence guidelines for the synthesis and application of more effective dopants. Recently, Lewis acid have been reported to show promising features as dopants for solution-processed polymers and small molecules.4 Here we propose a systematic study of different types of Lewis acids to investigate the potentiality of this doping strategy for organic field-effect transistors. Different processing routes and compositions will be studied in order to establish relevant structure/processing/property interrelationships.

BAS/1/1389-01-01

Materials science

1. Prepare solutions and learn coating methods for the formation of solution processable thin films. [Month 1-6] 2. Learn how to prepare and measure organic electronic devices, such as high emitting diodes, organic solar cells and field-effect transistor. [Month 3] 3. Study the influence of dopants in structural and optical properties of organic semiconductors and devices. [Month 4] 4. Prepare project updates reports and presentation. [Month 6]

Materials Science & Engineering

Physical Sciences and Engineering

KAUST Solar Center

Statistical models based on stochastic partial differential equations

Academic Program: Statistics

Biodiversity of Red Sea Reef Fishes

Academic Program: Marine Science

Connectivity of Shark Populations

Academic Program: Marine Science

Lactate signaling in synaptic plasticity

Academic Program: BioScience

Unraveling fungi community patterns in Red Sea coral reefs

Academic Program: Marine Science

The effect of rootstock-scion combination on microbiome selection by fruit plants

Academic Program: BioScience

Grafting is a common agronomic practice performed on different fruit plants such as, among others, apple tree, other Rosaceae and grapevine. It is used for improving the fruit cultivar adaptation to specific soil and for controlling some plant parasites. The rootstock affects scion development by influencing the reproductive performance, vigor, biomass accumulation and distribution in the plant, phenology and fruit yield. Moreover, a recent publication highlighted that rootstock type influences the recruitment by plants of microorganisms from the soil, which can be defined as the ‘seed-bank’ for root microbiome assemblages (Marasco et al., 2018, Microbiome 6:3. Doi 10.1186/s40168-017-0391-2). An aspect that is still overlooked is if and how the rootstock-scion combination affect the recruitment of the microbiome and their migration and colonization of the tissues in different plant compartments and organs. A deeper knowledge on this aspect is pivotal to better understand the factors steering the recruitment and the flux of microorganisms from the soil through the roots and within the plant compartments. Moreover, it would be interesting to unravel which rootstock-scion combinations can primarily influence the fruit quality, by inferring the functional diversity of the selected microbiome starting from high-throughput Illumina 16S rRNA gene sequencing. To investigate the community structure of the endophytic microbiome in different plant compartments (e.g. root, shoot, leaf, fruit) comparing several combinations of rootstock-scion pairs. The research will be conducted on fruit trees, e.g. grapevine, and the bulk soil (i.e. the soil not affected by root exudates) will be used as the reference microbial ‘seed-bank’.

BAS/1/1065‐01‐01

Plant-microbe interactions

-Creation of 16S rRNA gene libraries from soil and plant metagenomes for the description of the structure and taxonomy of the bacterial community associated to different plant compartments and to the bulk soil - Quali/quantitative identification of the bacterial taxa present in 16S rRNA libraries - Study of the alpha- and beta-diversity of the microbiome in different plant compartments according to the rootstock-scion combinations.

Biological and Environmental Sciences and Engineering

CRISPR-based genetic engineering of stem cells for in vitro production of human blood

Academic Program: BioScience

Multilevel and Unbiased Monte Carlo Methods for Option Pricing

Academic Program: Applied Mathematics and Computer Science

Online Outlier Detection for Functional Data

Academic Program: Statistics

Internship on Deep learning Methods for Satellite Data Downscaling

Academic Program: Statistics

Imaging the interfacial charge carrier dynamics at axial p-n junction nanowires

Academic Program: Chemical Science

To optimize the light harvesting, multiple junctions with different band gaps can be combined to match the solar spectrum, but lattice matching requirements severely limit the materials available for devices based on thin film growth. In general, nanowires (NWs) have emerged as building blocks for electronic and photonic technologies due to their distinct advantages over its bulk and planar counterparts. Multi-junction solar cells containing several p-n junctions which can be used to surpass the Shockley−Queisser efficiency limit in solar cells. However, the carrier dynamics at the interfaces of the p-n junctions are still not well understood. Being in this regime, the classical picture of photo-induced charge transfer at a heterojunction can be described as follows: right after excitation, electron-hole pairs are generated. This is followed either by electron-hole separation and drifting in opposite directions under the influence of the electric field of the p-n junction, or the electron-hole pairs can recombine radiatively by emitting a photon, or non-radiatively by carrier trapping or/and Auger recombination. However, where the exciton is localized and trapped, how the transfer occurs and how it depends upon the local environment is an important as yet unanswered question. The processes by which carriers transfer back across the interface and recombine, thereby returning to the equilibrium state, are highly dependent upon the local environment and are not fully understood and they cannot be catalogued using spectroscopic techniques. In other words, the accessibility of these dynamical processes by static imaging or steady-state and time-resolved spectroscopic techniques is very limited. The unique opportunity to visualize the carrier dynamics selectively on the material surface can only be accessed by 4D S-UEM with fs temporal and nm spatial resolutions. We will begin by utilizing the S-UEM technique to investigate charge dynamics in n-InGaN/p-GaN nanowires. For the S-UEM measurement, right after laser excitation, the time-resolved secondary electron images arising from the first few nm of the sample surface will be recorded, providing the image-contrast changes for mapping charge dynamics. Straightforwardly, bright or dark image contrast correspond to increase or decrease in the local electron density, telling us where the carriers (electrons/holes) are localized. In other words, bright and dark image contrast is interpreted as an increase in the local electron and hole densities, respectively. Moreover, charge separation and charge recombination can be directly accessed from the spreading out the bright contrast or from diminishing the dark contrast.

ASP/1/1669-01-01

Electron imaging, interface dynamics, oproelectronic applications

With real-space imaging, we can examine the temporal behavior of a local surface area (i.e., pixel areas), providing direct information about the charge carrier dynamics including trapping and recombination on the surface and at the interface of inorganic single crystals, different structural compositions of InGaN nanowires and the electron-hole localization across the p-n junction based on GaN/InGaN nanowires. The results would help us in gaining valuable insights into the photo-physics of such materials and guide in the better designing of optoelectronic devices

Chemical Science

Physical Sciences and Engineering

Real-Space Imaging of Perovskite Single Crystals Using 4D Electron Microscopy

Academic Program: Chemical Science

While charge carrier dynamics in the bulk of semiconductor materials are well understood, charge transport including ejection and diffusion on surfaces and interfaces is still one of the prime challenges facing the communities of solar cells and surface sciences. Unfortunately, time-resolved laser spectroscopies that have been commonly used to understand the dynamics of photo-generated carriers in condensed matter are limited by the large penetration depth (100s of nanometers) of their pump and probe pulses, making them only sensitive to the bulk properties of the investigated materials. Only rare, bespoke techniques based on ultrafast electron microscopies offer the surface sensitivity needed to track light-triggered carrier dynamics in real-time and space, such as four-dimensional scanning ultrafast electron microscopy (4D-SUEM) which has emerged recently as a powerful tool to investigate real space-time dynamics selectively, on top surfaces of various materials with high temporal and spatial resolutions. In 4D-SUEM, an optical laser pulse at 515 nm is used to excite the material surface; a pulsed primary electron beam is then generated through a delayed UV excitation pulse at 343 nm from the cooled Schottky field-emitter tip, emitting secondary electrons from the surface of the specimen in a manner that is extremely sensitive to the local electron/hole density at the surfaces and interfaces. Time-resolved secondary electrons images produced from the excited surface are detected and then analyzed, pixel-by-pixel. In this study, we will use 4D-SUEM to selectively map surface dynamics including carrier diffusion length of the MAPbI3 single crystals with different facets and different surface treatment. We will also try to image the interface of solar cells base on these single crystals including the charge carrier ejection to the electron and hole transporting layers as well as the impact of the oxide layers on the carrier dynamics and device performance

ASP/1/1669-01-01

Surface Dynamics Characterization

The finding of this project may offer a clear view of the extreme carrier diffusion behavior as a result of facet termination and surface treament. The results will be useful to address device performance bottle-necks, opening a new avenue to create MAPbI3 single crystal-based optoelectronic devices that take advantage of previously unknown, extreme surface behaviors.

Chemical Science

Physical Sciences and Engineering

Model- vs. Data-Parallelism for Training of Deep Neural Networks

Academic Program: Computer Science

Design and Control of Power Conversion Systems

Academic Program: Electrical Engineering

Continual Learning

Academic Program: Computer Science

Imagination Inspired Vision

Academic Program: Computer Science

Accelerated Chemistries in High-voltage Sprays of Water

Academic Program: Environmental Science and Engineering

Electrospray ionization (ESI) is a process that entails flowing liquids though metallic capillaries, typically held at voltages > 3 kV, leading to the formation of a fine spray of charged droplets (due to the imbalance between the cohesive surface tension and repulsive electrostatics). ESI is extensively used to characterize a diverse array of solutes, from simple ions to complex macromolecular complexes, using mass spectrometry (MS) - together, the platform is known as ESIMS. In the recent years,usingESIMS, researchers have reported that a number of chemical reactions accelerate by orders of magnitude, when carried out in ESI droplets in comparison to the reactions in the bulk phase. Examples include, the phosphorylation of glucose and ribose, Pomeranz-Fritsch synthesis of isoquinoline, the reaction between o-phthalaldehyde and alanine, and the ozonation of oleic acid. However, the mechanisms underlying these dramatic rate accelerations remain hotly debated. Most recently, using a variety of experimental and computational tools, we have demonstrated that ESI of solutions of water lead to gas-phase reactions, which are not representative of the air-water interface at thermodynamic equilibrium. 1 Now, we would like to develop an reactor based on ESI to investigate acid-catalyzed reactions towards the goal of producing high-value chemicals, for instance, of interest to the pharmaceutical industry.Specifically, the Summer Intern will investigate operational parameters, including the ionic strength and pH of water, ESI voltage, coaxial gas-flow rate, length of the reactor, etc., to optimize conditions based on reactions, such as the Fries Rearrangement of phenylacetate. We will analyze the reaction products through nuclear magnetic resonance and chromatography techniques. References:1. Gallo, et al., Chemical Science (2019) DOI: 10.1039/C8SC05538F

BAS/1/1070-01-01

Environmental and Materials Science

The VSRP intern will work with a senior graduate student and learn the following skills:• Laboratory experiments: ESI-MS, ESI reactions, voltagesources,identifying reactions of interest • Theory: basic electrostatics, data analysis, scientific writing We expect the intern to be knowledgeable in chemical engineering, driven by curiosity, hard-working, and thrive in a multicultural work environment.

Environmental Science and Engineering

Biological and Environmental Sciences and Engineering

Water Desalination and Reuse Center

Liquid marbles

Academic Program: Environmental Science and Engineering

How Insects-inspired Surfaces Prevent Wetting?

Academic Program: Environmental Science and Engineering

Diel Variations in the primary productivity of the upper ocean from autonomous glider and autonomous profiling float observations

Academic Program: Marine Science

Microfluidics-based single-molecule fluorescence imaging of nanoscopic cellular interactions

Academic Program: BioScience

Development of shortwave infrared emitting fluorophores and bioimaging application

Academic Program: BioScience

Numerical modeling of enhanced geothermal systems

Academic Program: Earth Science and Engineering

Stochastic analysis of multiple fracture propagation and interaction in reservoir rocks

Academic Program: Earth Science and Engineering

Joint models for longitudinal and survival data

Academic Program: Applied Mathematics and Computer Science

Impacts of UV radiation on corals and other organisms in the Red Sea

Academic Program: Marine Science

Biological stability of chlorinated and non-chlorinated drinking water

Academic Program: Environmental Science and Engineering

Characterization of biofilm growthrate in a membrane system

Academic Program: Environmental Science and Engineering

Re-defining the radiative efficiency limits of organic solar cells

Academic Program: Materials Science & Engineering

Hybrids and quantum dots for thermoelectric applications

Academic Program: Materials Science & Engineering

3D Bioprinting of Cell-Laden Microgels for the General Construction of Vascularized Tissue Structures and Organoids

Academic Program: BioScience

3D bioprinting is a fabrication technology that aims to produce complex 3D functional living tissues suitable for disease modeling and even organ transplantation. Consequently, this technology has gained strong interest in areas such as tissue engineering and regenerative medicine. It is believed that 3D bioprinting gives promising solutions to solve the organ shortage for transplantation and to bridge the gap between 2D cell culture and live tissue experiments. In 3D bioprinting technology, biomaterials and cells are printed together to produce viable tissue-constructs with different shapes and sizes at the micro- and macro-scale level. However, 3D bioprinting technology is currently facing problems in terms of poor performance in shape fidelity of the 3D constructs, biocompatibility, and vascularization. These drawbacks could arise from the biomaterial and the bioprinting method used. For instance, synthetic polymers lack cell adhesion motifs while naturally derived materials lack appropriate mechanical strength. Moreover, most of the bioprinting methods rely on UV- or chemical crosslinking that could be harmful to the cells. To overcome these problems, we have developed a 3D bio-printing method to print under physiological conditions using self-assembling ultrashort peptides (SUP) as bioinks. SUP are natural peptides that are chemically synthesized and can be tailored to include biological and physicochemical cues for the improvement in biocompatibility and shape fidelity of the 3D construct. Nevertheless, our bioprinting approach still has technical challenges in controlled cell distribution and vascularization. To address these issues, we are working on the incorporation of SUP microgels covered by endothelial cells into the 3D bioprinting process to drive the cell to cell connection among microgels.

BAS/1/1075-01-01

Materials Science

Print self-assembling ultrashort peptides (SUP) as bioinks

Biological and Environmental Sciences and Engineering

Computational Bioscience Research Center

Modelling and numerical simulation

Academic Program: Applied Mathematics and Computer Science

MPS modelling of carbonate rocks

Academic Program: Earth Science and Engineering

Vertical and Lateral Heterogeneity in Unconventional Source Rock Sequences

Academic Program: Energy Resources and Petroleum Engineering

Economic production of a hydrocarbon reservoir is critically influenced by a good understanding of the distribution of litho- and organo-facies, porosity, geochemical and geomechanical properties of the reservoir layers. This, in turn, is primarily a function of both the vertical and lateral heterogeneity in the distribution and geochemical composition of soft organic-rich layers and more brittle organic-lean layers, taking in an account the regional assessment of the stress regime, heatflow and thermal conductivity. The proposed study will help to conduct a detailed assessment on a production scale of depositional and geochemical heterogeneities with unconventional development scenarios. The main aim of this research project is to describe and analyze the vertical and lateral heterogeneities of unconventional reservoirs through outcrop analogue investigations, in order to: 1. characterize the lateral and vertical changes in cyclicity and its impact on the occurrence, distribution and geochemical composition of organic-rich and organic-lean facies in a basinal setting; 2. provide based on the results of the fieldwork an assessment of possible variations in production behaviour of unconventional reservoirs in rocks of similar origin. The candidate will perform, along with fieldwork, a variety of microscopic and geochemical analyses on systematically collected sample from different outcrops exposing the Upper Cretaceous-Eocene Rocks of Jordan, which has a great potential as an unconventional reservoir.

BAS/1/1399-01-01

Geology, Carbonate Geology, Sedimentology, Stratigraphy, Unconventional Source Rocks, Facies Analysis

1. The project will form a Masters Thesis of the proposed student. 2. The results will published in peer reviewed Journals. 3. The results will be presented in international conferences.

Energy Resources and Petroleum Engineering

Physical Sciences and Engineering

Ali I. Al-Naimi Petroleum Engineering Research Center

Micritization under elevated temperature and pressure

Academic Program: Applied Mathematics and Computer Science

Facies architecture of a carbonate Island (Al Wajh bank, Red Sea)

Academic Program: Energy Resources and Petroleum Engineering

Pleistocene to Holocene sediment dynamics of the Al Wajh Bank slope (Red Sea)

Academic Program: Earth Science and Engineering

Pleistocene to Holocene architecture of Dolphin Island (Al Wajh, Red Sea)

Academic Program: Earth Science and Engineering

Expression, Purification, Characterization of Proteins for Biocatalysis

Academic Program: BioScience

Functionalization of Gas Vesicles

Academic Program: BioScience

Visible-light Photoredox Catalysis Transformations

Academic Program: Chemical Science

Tackling the challenges of OH-Laser Induced Fluorescence technique on detonation: numerical approach to improve experimental design

Academic Program: Mechanical Engineering

Context: 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. In parallel to its promising application to the energy production field, detonation studies have also regained interest for safety applications, due to the last nuclear accident in Fukushima. Thus, two aspects can be distinguished: for transportation, researchers are focused on obtaining and controlling a self-sustained detonation in a specific engine (PDE or RDE), while for industrial safety, researchers are focused on identifying the detonation limits and the quenching mechanism of detonation to prevent them. 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 (OH-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

BAS/1/1396-01-01

Mechanical engineering, chemical engineering, aerospace engineering

First, the student will have to become familiar with the principle of the OH-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

Clean Combustion Research Center

Temperature of hydrogen-air detonations: measurements by 2-color planar laser induced fluorescence of the hydroxyl radical

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. For a long time restricted to military applications, due to the global energetic issue, the civil applications of detonations have received in increasing interest during the last decade. One of the main challenges in this research field is to obtain a self-sustained detonation for practical fuel-oxidizer mixtures (e.g. kerosene-air), in a setup with typical dimensions comparable to those of a commercial gas turbine. While the quantitative characterization of flames in terms of temperature and chemical species is of current practice, the experimental study of detonation properties is mainly restricted to the determination of the detonation velocity, global pressure, and density gradient structure. Information such as the temperature or density of hydroxyl radical fields in a detonation front have never been measured. However, to better understand the detonation mechanisms and to help in validating detonation models and numerical simulations, these data are crucial. Objectives: In this context, the main objective of the project is to adapt a non-intrusive thermometry technique broadly used in the combustion community, the 2-color planar laser induced fluorescence on OH, to the characterization of an H2 – air detonation. There are many challenges to obtain reliable temperature measurements of a detonation front, including single shot measurements, synchronization, spectroscopic properties of OH in the condition of the detonation front, etc.

ASP/1/1669-01-01

Mechanical Engineering, Combustion, Fluid Mechanics

First, the student will learn the 2-color PLIF technique on OH in a well characterized configuration: a stable flat flame stabilized over a McKenna burner. During this preliminary step, the uncertainty of the measurements, averaged and single shot, will be characterized. Second, the system will be implemented on a 2D detonation test rig, equipped with optical access for laser diagnostics. One of the main challenges will be the synchronization of the PLIF system with the detonation front arrival in the measurement area. Indeed, the detonation front propagates at a speed of about 2000 m/s, and the jitter between two events in the arrival of the measurement area can be as large as 1 ms. Synchronization will require time of flight measurements and analysis of the detonation propagation front. Finally, post processing of the OH PLIF images will be performed in order to determine the temperature fields. Due to the complexity of the detonation setup and combined laser diagnostics, the experimental part of this study will be performed by two persons: the intern student and an experienced researcher (senior PhD student or post-doc).

Mechanical Engineering

Physical Sciences and Engineering

Clean Combustion Research Center

Gradient compression for distributed training of machine learning models

Academic Program: Computer Science

Towards a Principled Understanding of Deep Learning

Academic Program: Computer Science

Federated Learning

Academic Program: Computer Science

Topics in Machine Learning and Optimization

Academic Program: Applied Mathematics and Computer Science

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

Visual Computing Center

Mapping protein complexes in vivo

Academic Program: Plant Science

Molecular mechanisms underlying growth and defense in plants

Academic Program: Plant Science

Spatio temporal analysis of expression of genes controlling assymetric stem cell division and tissue patterning in plants

Academic Program: Plant Science

Large-Scale Scientific Visualization

Academic Program: Computer Science

Experimental study of carbon-free combustion

Academic Program: Mechanical Engineering

Passivation in Perovskite Solar Cells

Academic Program: Materials Science & Engineering

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

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

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

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

Novel Ultrashort Peptide Nanogels Generate Silver Nanoparticles to Combat Emerging Antimicrobial Resistance Strains

Academic Program: Computer Science

Developing an In Vitro 3D Tissue Model for Studying Alzheimer’s Disease

Academic Program: Computer Science

Alzheimer’s disease is a degenerative disease where patients gradually lose their memory and cognitive skills due to the death and degeneration of neurons. Neurons are the cells responsible for transmitting information inside the brain through electrical charges and neurotransmitters, which are chemicals that flow across the synapses between each neuron. The main features of Alzheimer’s disease is described to be the formation of abnormal structures called beta amyloid plaques (formed outside of the cells) and neurofibrillary tangles (formed inside the cells). These plaques and tangles damage the interior and structure of neurons, causing their destruction and demise. 2D cultures are commonly used in a wide range of in vitro research studies, however, they do not resemble in vivo conditions, where cells would live and interact within a complex three-dimensional (3D) microenvironment. For this reason, 3D cultures were developed, so that they can provide a close representation or simulations of tissues in the living organism. Considering the mentioned approaches, we propose to develop a 3D model to generate the main two pathophysiological features of Alzheimer’s disease –plaques and tangles. The project aims to set a standard model to form plaques and tangles efficiently, and be able to target the aggregation of beta amyloids and tau proteins to prevent further aggregation in the future.

BAS/1/1675-01-01

Alzheimer’s, amyloids, disease model, neurodegenerative diseases

Developing in vitro assays such as Thioflavin assay to follow up on plaque and tangle formation (Hauser), using selected short amyloid peptides to demonstrate amyloid formation (Hauser), study amyloidogenesis (Hauser,Michels), molecular dynamic computational simulations (Michels)

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Visual Computing Center

3D Bioprinting of Multicellular Constructs for the Development of Vascularized Skin Grafts

Academic Program: Computer Science

Inverse Problems in Imaging

Academic Program: Computer Science

Computational Cameras

Academic Program: Computer Science

In-liquid Visualization of Phenol during Plasma Purification

Academic Program: Mechanical Engineering

Molecular hospitality seen by NMR spectroscopy

Academic Program: BioScience

Deciphering the molecular mechanisms of DNA replication and repair – integration of computational and experimental approaches

Academic Program: BioScience

Genomic DNA is under constant assault by environmental factors that introduce variety of DNA lesions. The cell evolved several DNA repair and recombination mechanisms to remove these damages and ensure the integrity of the genomic DNA. These mechanisms use DNA structures that deviate from the heritable duplex DNA (flaps, nicks, gaps, bubbles and four-ways Holliday junctions) as common pathway intermediates. However, these structures are extremely toxic since they break the continuity of the heritable duplex DNA and impose impediment to replication and transcription. Members of 5’nuclease excise these aberrant DNA structures during replication, repair and recombination. It is not surprising therefore that mutations in members of 5’nucleases have been linked to various disease states including cancer and aging. Furthermore, some of these nucleases are highly over-expressed in several cancers to compensate for deficiencies in their damage response pathways. Despite the importance of 5’nuclease it remains unclear how they recognize normal DNA sequence just based on their structure and precisely cleave them. The knowledge gap in structural studies that can access protein and DNA dynamics in 5’nucleases impairs substantially the drug development enterprises against numerous severe human cancers. The proposed research project within the framework of the VSPR (Visiting Student Research Internship Program) program is mainly focused on the molecular bases of substrate recognition by 5’nucleases. The project combines cutting-edge computational resources and state-of-the-art biophysical computational tools, including full-atom molecular dynamics (MD) simulations, to establish the conformational states and dynamics of bubble DNA structure and how they are influenced by the bubble size and DNA sequence. DNA bubbles structure is the key intermediary step during nucleotide excision repair that separate the strand containing the lesion site from the intact one before two members of 5’nucleases, XPG and XPF, perform two concerted cleavages to release the damage-containing ssDNA. Establishing the bubble conformer(s) will pave the way for better understand of its interaction with XPG and XPF. These studies will be accompanied and verified side-by-side by experimental results derived from the cutting-edge biophysical techniques, including single-molecule FRET (smFRET) and high-resolution multidimensional Nuclear Magnetic Resonance (NMR) experiments. In one alleged model, the bubble DNA structure might display dynamic conformations and the nucleases involved in NER simply capture the correct conformer. In another model the bubble might have stable conformer(s) and the nucleases actively bind to them and mold them into the ''correct'' conformer. The computational work, like full-atom molecular dynamics simulations assisted with the experiments smFRET and NMR data would help in decoding the actual mechanism of action of XPG and XPF against the bubble DNA. This project is suitable for students with bioscience, physics, or engineering background.

BAS/1/1085-01-01

Structural Biology, Computational Biology, Protein-DNA interactions, FRET, NMR, Molecular Dynamics (MD), Biophysics

We offer the students participating in this project to learn:how to use the biophysical techniques and design the workload;how to become self-reliant in the design, preparation, running and analysis of the molecular dynamics (MD) simulations of biologically essential macromolecular systems;how to analyze and integrate the results derived from different computational techniques with the experimentally derived information from the cutting-edge biophysical techniques, including smFRET and Bio-NMR.To complete the above tasks we expect from the students:A positive approach and strong will to work in the interdisciplinary and international research team;the preparation of the final report in the article format;the preparation of presentation that will be given during the group meeting.

Biological and Environmental Sciences and Engineering

Why it is so difficult to find potent drugs against cancer? - decoding the druggability of molecular targets

Academic Program: BioScience

The drug discovery process aims at finding novel, more potent small molecules (ligands) that can treat/cure certain human diseases, being a therapeutic agent, or serve in diagnosis of the early stages and progression of the disease. Despite the extensive joint efforts of many laboratories around the world, developing a potent drug is still a major and often a daunting challenge. As of now only 2% of human proteins interact with the currently approved drugs. On the top of that only 10% of human proteins are relevant to the disease. If a drug interacting with the target protein is known, and this interaction leads to therapeutic benefits of patients, then this target protein is called druggable. By simple approximation one can expect that other similar proteins, belonging to the same or related families, can be targeted by ligands - meaning: are druggable. Unfortunately it is often not a case, and only 10-15% of the human genome is predicted to be druggable, with only half of the targets being essential to any disease process.Our group is focused on understanding the molecular determinants driving the protein-protein and protein-ligand interactions, thus druggability of protein targets. We focus our attention on the closely related groups of proteins involved in the gene regulation processes, like histone methyltransferases and demethylases. These proteins covalently modify flexible tails of histones by attaching or removing the -CH3 groups to the side-chains of lysines and arginines. This way they modulate the accessibility of the DNA double strand - genes - toward transcription factor proteins and RNA polymerase. The miss-function of methyltransferases and demethylases is well known to lead to numerous severe cancers in human, like acute leukemia, variety of gastric carcinomas as well as several mental disorders, like schizophrenia.With the combination of molecular biology approaches we prepare and purify the human proteins of interest and subsequently study their structures and interactions with known drugs and other small molecules – ligands - potential candidates for becoming more potent drugs. In the lab we use multidisciplinary approaches and combine several state-of-the-art molecular biology and biophysical techniques. Our main technique, used and developed in the group, is the cutting-edge high-resolution nuclear magnetic resonance (NMR) spectroscopy. The conformational studies in solution are supplemented by other experimental biophysical methods, like X-ray crystallography, isothermal titration calorimetry (ITC), circular dichroism spectroscopy (CD), as well as advanced computational approaches - molecular dynamics (MD) simulations.In conclusion, with the combination of experimental and computational data for selected families of disease related proteins, we try to understand the molecular determinants that make closely related proteins druggable or not druggable.The proposed interdisciplinary project can host two VSRP students.The project offers the wide range of experience, from the protein biochemistry techniques that lead to preparation of biologically relevant material, followed by hands-on experience, i.e. from the design and performing the advanced nuclear magnetic resonance NMR experiments to preparation and running the comprehensive molecular dynamics MD simulations and finally integration of the results coming from the different fields of expertise.Students with the background in bioscience, physics and engineering, who enjoy working in the international and interdisciplinary environments, are welcomed to apply.

BAS/1/1084-01-01

bioscience, molecular biology, protein biochemistry, nucleic acids – DNA, computational structural biology, nuclear magnetic resonance NMR, epigenetics

We offer the students participating in this project to learn: •how to efficiently work in the state-of-the-art protein biochemistry lab, prepare own protein(s) for the biophysical and spectroscopic (e.g. NMR) studies and design the workload; •how to become self-reliant in the design, preparation, running and analysis of the advanced multidimensional NMR experiments, molecular dynamics (MD) simulations of biologically essential macromolecular systems; •how to analyze and integrate the results derived from different computational techniques with the experimentally derived information from the cutting-edge biophysical techniques, including Bio-NMR and X-ray. To complete the above tasks we expect from the students: • a positive approach and strong will to work in the interdisciplinary and international research team; •the preparation of the final report in the article format;•the preparation of presentation that will be given during the group meeting.

Biological and Environmental Sciences and Engineering

Investigation of nanoparticulate morphology at PPC and HCCI modes

Academic Program: Mechanical Engineering

Development of algorithms to decipher the complexity of chromation organization

Academic Program: BioScience

Advanced Wastewater Treatment for Water Reclamation and Reuse

Academic Program: Environmental Science and Engineering

Fouling in Membrane Filtration Systems

Academic Program: Earth Science and Engineering

Modeling structure and properties of transition metal complexes

Academic Program: Chemical Science

Complex optoelectronics materials and phenomena

Academic Program: Electrical 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

Cutting-edge genomics to unravel the genetic basis of disease resistance in crop plants 2

Academic Program: Plant Science

Cutting-edge genomics to unravel the genetic basis of disease resistance in crop plants

Academic Program: BioScience

Identifying the genetic drivers of thermal tolerance plasticity in the coral model Exaiptasia

Academic Program: Marine Science

Improving coral thermal tolerance through association with acclimatized Symbionts

Academic Program: Marine Science

High resolution remote sensing of agricultural systems for improved water and food security

Academic Program: Environmental Science and Engineering

Polymeric membranes for liquid separation

Academic Program: Environmental Science and Engineering

An iPSCs-based approach to model Type Two Diabetes in-vitro

Academic Program: BioScience

Large-scale scholarly data mining

Academic Program: Computer Science

Anaerobic membrane bioreactor as a decentralized municipal wastewater treatment technology

Academic Program: Environmental Science and Engineering

Screening for Carotenoid-Derived Signaling Molecules

Academic Program: BioScience

Protein Synthetic Biology

Academic Program: BioScience

Structural Landscape of Genetic Diseases

Academic Program: BioScience

Structural Biology of Immune Signalling

Academic Program: BioScience

Simultaneous Communications and Localization Low Data Rate Low Cost Sensing Systems

Academic Program: Electrical Engineering

GNSS attitude determination and precise positioning

Academic Program: Electrical Engineering

Cutting-edge red light- emitting diodes by nitride semiconductors

Academic Program: Electrical Engineering

Water splitting to produce clean hydrogen energy by semiconductor band engineering

Academic Program: Electrical Engineering

Making ML-based Networked Systems more Trustworthy

Academic Program: Computer Science

Machine learning (ML) solutions to challenging networked systems problems are a promising avenue but the lack of interpretability and behavioral uncertainty affect trust and hinder adoption. The goal of this work is to develop new solutions to facilitate the training of robust ML-based decision-making agents for systems. With their recent advancements, today’s ML solutions provide remarkable results in many fields. ML methods have been recently applied to several networked systems problems including routing, congestion control, resource allocation, flow scheduling and video rate adaptation. In particular, Reinforcement Learning (RL) has been a main approach due to its ability to directly learn from experience and simulation without relying on labeled datasets, and to optimize any desirable metric. Yet, there is a general fear that ML systems are black boxes: closed systems that receive an input, produce an output, and offer no clue why. This creates uncertainty about why these systems work, whether they will continue to work in conditions that are different from those seen during training or whether they will fall off performance cliffs. Given their statistical nature, it is difficult to predict how ML-based agents will behave when faced with previously unseen inputs. Because of this uncertainty, it is not possible to let these agents control critical, large scale systems without safety guarantees, despite the evidence that ML solutions can yield better efficiency and resource utilization than classical approaches. We seek to address this problem.

ASP/1/1669-01-01

Computer Science

The goal of this internship is to verify and improve existing ML agents. The student will be expected to learn about existing solutions, as well as the challenges and requirements to applying ML techniques in their settings. With guidance of other team members, the student will then find new solutions for either verifying the safety of ML agents or improve their behavior in order to reduce risks related to the safety and stability of the agent’s decisions. Possible directions include (1) analyzing the agent behavior based on symbolic or concolic execution, (2) exploring the evolution of the agent’s behavior during training, (3) considering new ways of modifying a trained agent to meet stricter requirements. Candidates should be motivated to work on research-oriented problems with a team and develop new solutions in a budding field. They should have a strong background in computing and software engineering, in particular with regards to machine learning, networking, and distributed systems. Ideally, they should have experience in building machine learning solutions and working with related tools, as well as knowledge of Python.

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

High-efficiency AI and ML distributed systems at Big-Learning scales

Academic Program: Computer Science

The position will be in the context of a project whose goal is to make distributed AI and ML more efficient. Enabled by the rise of big data, today’s AI and ML solutions are obtaining remarkable success in many fields thanks to their ability to learn complex models with millions to billions of parameters. However, these solutions are expensive because running AI and ML algorithms at large scales requires clusters with tens or hundreds of machines to satisfy the high computation and communication costs of these algorithms.Many systems exist for performing AI and ML tasks in a distributed environment. Yet, the performance requirements and input data sizes are steadily growing. The next level of efficiency is required to address key challenges like network communication bottlenecks and uneven cluster performance.Moreover, the fidelity of ML models is very sensitive to many hyperparameters. To produce accurate models, it is of great importance to tune these hyperparameters well. However, this requires exploring a large space of possible configurations, which must be done efficiently. The internship work will generally integrate in the current challenges faced during the project whether that is to investigate the trade-offs between reduced communication and model precision or to identify the bottlenecks and develop new algorithms to overcome them.Ongoing directions are (1) exploring the use of new networking hardware and architectures to make network-based communication more efficient and (2) designing new search algorithms that can make a better use of resources and determine optimized hyperparameters more efficiently.Candidates should be motivated to work on research-oriented problems in a fast-paced and tight-knit team. They should have a strong computing or engineering background with a good background in algorithms, machine learning, distributed systems, and networking. Ideally, they would have experience in building and working with large software systems and tools, and proven knowledge of C++/Java.

BAS/1/1673-01-01

Computer Science

The students are expected to study the existing solutions and devise theoretically-sound approaches (with the assistance of the supervisor) to improve their performance. The students will be able to also collaborate with other team members and to evaluate the mechanisms on real-world datasets on a state-of-the-art testbed. The above results, if completed, are considered novel and can result into a publication (with the agreement of the supervisor).

Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Machine Learning for Graphs

Academic Program: Computer Science

Machine Learning for Biological and Medical Imaging

Academic Program: Computer Science

Algorithmic Information Theory for Machine Intelligence

Academic Program: Computer Science

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.

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.

Biological and Environmental Sciences and Engineering

Visualization of time dependent oil displacement from core plugs by X-ray CT

Academic Program: Energy Resources and Petroleum Engineering

Visualization of Oil Displacement Patterns in Micromodels

Academic Program: Energy Resources and Petroleum Engineering

Numerical modelling of capillary driven flow using OpenFOAM

Academic Program: Energy Resources and Petroleum Engineering

Big Data: Mess versus Value

Academic Program: Chemical Science

The problem of “messy” drilling and downhole data, and how to extract maximum value from it, is a pertinent challenge for the petroleum industry – in particular when it comes to advancing the understanding of near‐wellbore physics and chemistry. This is particularly important since technological advancements, workflow optimizations and integrated project processes/execution are directly linked to a reduction in the cost of well construction, which generally is the largest expenditure item during field development. We are looking for a highly motivated bachelor or master student who will be responsible for loading, processing, and analyzing continuous data streams from wellbores acquired with sensors during the drilling and reservoir monitoring process (e.g., measurement or logging while drilling). The purpose is to find in and extract from these data key information necessary to optimize drilling and improve our understanding of the processes ongoing in the near wellbore region.Results from this internship project will be integrated into the development of automation systems that are capable of handling massive data streams from drill sites, maximize the quality of these data streams, find key information necessary to optimize drilling (i.e., lower cost), and help reduce risk, lost and non‐productive time (i.e., prevent failures and accidents).We expect that this research will lead to publications, which the student can contribute to.

BAS/1/1378‐01‐01

Drilling, production, or reservoir engineering.

We are seeking a B.Sc. (Bachelor of Science) or M.Sc. (Master of Sciences) student who is interested in the stated topic for his / her thesis research. The project is suitable for candidates interested in rock mechanics, geo‐chemistry, or/and data analysis / statistics.

Chemical Science

Physical Sciences and Engineering

Ali I. Al-Naimi Petroleum Engineering Research Center

Development of Electrochemical Sensors for Environmental Monitoring

Academic Program: Chemical Science

Fano Resonant Gas Sensors

Academic Program: Electrical Engineering

Hardware Benchmarking of Deep Neural Networks

Academic Program: Electrical Engineering

A High Level Synthesis Framework for Spiking Neural Networks

Academic Program: Electrical Engineering

Brain Inspired Computing

Academic Program: Electrical Engineering

Biomedical Sensors

Academic Program: Electrical Engineering

Internship on Statistical Methods for Brain Signals

Academic Program: Applied Mathematics and Computer Science

Development of a novel Stimulated Raman Scattering microscopy system

Academic Program: BioScience

Novel Micro-optical structures on optical fiber tip with two-photon lithography

Academic Program: BioScience

Ultraviolet materials and devices

Academic Program: Electrical Engineering

Superluminescent diodes

Academic Program: Electrical Engineering

High speed photodetector

Academic Program: Electrical Engineering

Underwater Optical Communication

Academic Program: Electrical Engineering

Solar Hydrogen Fuel Generation

Academic Program: Electrical Engineering

Underwater wireless optical communications

Academic Program: Electrical Engineering

Teaching Cars to Drive and UAVs to Race

Academic Program: Electrical Engineering

At the Image and Video Understanding Lab (IVUL) at KAUST, we have developed a photo-realistic simulator (denoted UE4Sim) based on the open-source computer game engine Unreal Engine. The UE4Sim simulator has been designed to facilitate the integration of computer vision and machine learning techniques into a realistic looking 3D environment with the following advantages. (1) By facilitating the generation of 3D worlds, UE4Sim enables the quick and automatic acquisition of large amounts of labelled data to be used for training data-hungry machine learning models (specifically deep neural networks) targeting a multitude of computer vision and machine learning applications ranging from self-navigating cars/drones and aerial tracking to indoor 2D/3D scene understanding and 3D reconstruction. (2) UE4Sim provides simple-to-use connections with third party software to allow for real-time evaluation of AI techniques. The photo-realism of UE4Sim facilitates the transfer of the learned models to the real-world. In this internship project, we plan to develop UE4Sim further, motivated by the goal of teaching a car to drive in previously unseen scenarios and unmanned aerial vehicles (UAVs) to race through obstacle courses. All this functionality will be done within UE4Sim with an ultimate aim at transfer this learned knowledge to real world vehicles.

BAS/1/1653-01-01

Electrical Engineering or Computer Science

Improvements on the UE4Sim simulator to make it more streamlined, efficient, and developer friendly for future development and integration with various types of learning (e.g. deep learning and reinforcement learning)Development of deep learning methods to estimate future positions of the vehicles (called waypoints) directly from images (perception network) Development of reinforcement learning methods to generate the appropriate vehicle controls, e.g. steering wheel angle or pitch/yaw/roll (control network) System integration of the perception and control network within UE4Sim

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Visual Computing Center

Persistent Automated Tracking from Unmanned Aerial Vehicles (UAVs)

Academic Program: Electrical Engineering

Empowering unmanned aerial vehicles (UAVs) with automated computer vision capabilities (e.g. tracking, object/activity recognition, etc.) is becoming a very important research direction in the field and is rapidly accelerating with the increasing availability of low-cost, commercially available UAVs. In fact, aerial tracking has enabled many new applications in computer vision (beyond those related to surveillance) including search and rescue, wild-life monitoring, crowd monitoring/management, navigation/localization, obstacle/object avoidance, and videography of extreme sports. Aerial tracking can be applied to a diverse set of objects (e.g. humans, animals, cars, boats, etc.), many of which cannot be physically or persistently tracked from the ground. In particular, real-world aerial tracking scenarios pose new challenges to the tracking problem, exposing areas for further research. Visual tracking on UAVs is a very promising application, since the camera can follow the target based on visual feedback and actively change its orientation and position to optimize for tracking performance. This marks the defining difference compared to static tracking systems, which passively analyze a dynamic scene. In this project, we will develop novel tracking strategies that are designed for real-time operation on a UAV. These tracking methods should be fast, reliable, and accurate. For evaluation purposes, we will use the newly developed aerial tracking benchmark that the IVUL group has developed. Moreover, we will test out these trackers within the aerial simulator that the IVUL group developed based on a photo-realistic game engine and a VR setup, which allows the user to move the object to be tracked in the simulated environment. Finally, these tracking methods will be embedded in a fully functioning UAV, which will be able to automatically and persistently track an object of interest on the ground.

BAS/1/1653-01-01

Computer, Electrical , Mathematical Sciences , Engineering

Statistics on the nuisances commonly faced in aerial tracking scenarios. Novel techniques to track a single object from a single aerial viewpoint. Novel techniques to search for an object when it moves outside the field of view of the camera. A fully functioning prototype UAV that runs the tracking method locally and that interfaces tracking results into the UAV’s navigation system.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Visual Computing Center

Large-Scale Human Activity Recognition in the Wild

Academic Program: Electrical Engineering

With the growth of online media, surveillance and mobile cameras, the amount and size of video databases are increasing at an incredible pace. For example, YouTube reported that over 300 hours of video are uploaded every minute to their servers. Moreover, the commercial availability of inexpensive cameras has led to an overwhelming amount of data, of which video streams from surveillance systems have been quoted to be the largest source ofbig data. However, the significant improvement in camera hardware has not been paralleled with accompanying automated algorithms and software that are crucial to intelligently sift through this ever-growing data heap. This situation has become so dire that much of large-scale video content (either online or in local networks) is rarely processed for meaningful semantic information. With such a development, this data merely serves as dense video sampling of the real-world, which is void of connectivity, correlation, and a deeper understanding of the spatiotemporal phenomena governing this data. Arguably, people are the most important and interesting subjects of these videos. The computer vision community has embraced this observation to validate the crucial role that human activity/action recognition plays in building smarter surveillance systems (e.g. to monitor public safety and public infrastructure usage), as well as, to enable business intelligence, semantically aware video indices (e.g. intelligent video search in large databases), and more natural human-computer interfaces (e.g. teaching robots to perform activities by example or controlling computers with natural body language). However, despite the explosion of video data available, the ability to automatically detect, recognize, and represent human activities is still rather limited. This is primarily due to impeding challenges inherent to the task, namely the large variability in execution styles, complexity of the visual stimuli in terms of camera motion, background clutter, and viewpoint changes, as well as, the level of detail and number of activities that can be recognized. In this project, we will address the important problems of human activity detection/classification, summarization, and representation with a suite of algorithms that are capable of efficiently and accurately learning from a newly compiled large-scale video dataset equipped with descriptive, hierarchical, and multi-modal annotations, calledActivityNet. We will investigate different facets of these problems with the ultimate goal of improving state-of-the-art performance in detecting and classifying human activities in real-world videos at large-scales.

BAS/1/1653-01-01

Computer, Electrical , Mathematical Sciences , Engineering

Novel techniques to classify snippets of video according to the activities they entail Novel techniques to quickly localize “activity proposals”, i.e. temporal segments in video where the probability of finding interesting activities is high. Combining the knowledge of objects and scenes in classifying an activity, since an activity is a spatiotemporal phenomenon where humans interacts with objects in a particular place Crowd-sourcing framework (e.g. using Amazon Mechanical Turk) to cheaply extend the annotations of ActivityNet to object and place classes, as well as, free-form text description. These annotations will enrich the dataset, forge links with other large-scale datasets, and enable new functionality (e.g. textual translation of a video that enables text queries).

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Visual Computing Center

Augmented Reality with Google Tablets and Glasses

Academic Program: Electrical Engineering

Wearable devices have attracted a lot of attention from the research community. These devices enable access to a user’s day-to-day life. Many of these devices support multi-modal sensors that can record and/or transfer sensory data including video and audio. Coupling this source of data with network connectivity can enable a wide range of augmented reality (AR) applications, which serve to enrich the user’s life and provide more insight in making decisions. For example, a wearable device supported by intelligent computer vision and machine learning methods can automatically infer and relay information about the place and situation to the user directly. This provides the user with more information to make a particular decision, e.g. whether or not to buy a product in a store based on reviews and competitor pricing found online. Moreover, these augmented capabilities could be very beneficial for people with sensory impairments, e.g. a visually impaired person wearing an AR device can be warned (through audio) of immediate obstacles in his/her way or a hearing impaired person can be notified (through words on a display) of someone calling out to him/her. In this project, we aim to build an AR system based on the Google Glass and a Google Tablet, which will automatically acquire visual and audio data and transfer it to a central processing station for analysis. Information inferred from this data will be transferred back to the Glass, so that it is conveyed to the user in visual or audio form. This is possible because the Glass supports both visual and audio sensors. One possible output of this project is the ability to project on the Glass display automatically-generated results of recognizing (i.e. labeling) and detecting (i.e. localizing) objects in front of the user.

BAS/1/1653-01-01

Computer, Electrical , Mathematical Sciences , Engineering

A software module based on the Google Glass SDK to acquire and transfer still images and videos from the Glass to a central processing station and transfer meta-data in the opposite direction. An API for the central processing station to invoke automatic computer vision and machine learning algorithms on the received images and videos. A software module based on the Google Glass SDK that conveys to the user the meta-data acquired from the central processing station on the Glass display. A large-scale dataset of videos and still images captured by a Google Glass during day-to-day activities. The important objects and activities in these videos will be manually labeled and used for training as well as testing the overall system. This dataset will be made publicly available to the research community for future algorithm evaluation and comparison.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Visual Computing Center

Experimental validation of new neuro-vascular fractional model

Academic Program: Electrical Engineering

Shape recognition using squared eigen-functions of the Schrödinger operator

Academic Program: Electrical Engineering

Optimization, control and monitoring of solar driven desalination systems

Academic Program: Electrical Engineering

Membrane distillation (MD) is a thermally driven distillation process. In this process, hot feed stream is passed along one side of a hydrophobic membrane, which is only permeable for water vapor and retains liquid water, whereas the other side is kept at a lower (cooler) temperature. Due to temperature difference across the membrane, water evaporates at the feed-membrane interface and the induced partial vapor pressure difference drives only water vapor through the membrane where it condenses on the other side of the membrane, called the permeate side. MD requires low-grade heat, which can be harvested from solar thermal energy, and other renewable or waste heat sources. Also, unlike the well-known reverse osmosis, MD operates at a lower water pressure, which in turns reduces the capital and operational costs. All these advantages make MD ideal for remote area desalination plants installations with minimal infrastructure and less demanding membrane characteristics. However, MD is faced with challenges that are yet to be addressed in order for this technology to be competitive with conventional desalination techniques. In recent years, MD has been coupled with renewable energy sources, such as solar thermal collectors and photovoltaic (PV) panels, to capitalize on the attractive features of MD. However, the unsteady nature of renewable energy sources imposes a challenge on solar powered membrane distillation (SPMD) that requires special attention on process modeling and system control. Moreover, over time, membrane permeability changes due to scaling and fouling. All these factors have to be taken into consideration when modeling MD. In this project, the student will design an optimal control strategy to control the productivity of the combined solar-MD system under different constraints. He will also design monitoring strategies for fouling detection using estimation methods. Experimental validation will be performed in collaboration with Water desalination and Reuse center at KAUST.

BAS/1/1627-01-01

Taous-Meriem Laleg-Kirati

Electrical engineering/ Applied Mathematics/Control Theory

Controller will be deigned and tested by simulations and if time will allow experiments will be performed.- A paper will be submitted

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

Computational Bioscience Research Center

Wireless Networks for Better Smart Buildings

Academic Program: Computer Science

Realtime transmission in Underwater Wireless Environments

Academic Program: Computer Science

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

High Pressure Heterogeneous Autoignition of Hydrocarbon and Syngas Fuels

Academic Program: Mechanical Engineering

CESE solver on GPUs for real gas flow simulations

Academic Program: Applied Mathematics and Computer Science

The project focuses on the extension to more complex thermodynamic models and equation of states of a two and three dimensional compressible fluid dynamic solver for the Euler and Navier–Stokes equations. Specific interest are developed to the implementation and use of the polytropic van der Waals (PvdW), and the polytropic Peng-Robinson (PR) models in one of the compressible solver available in our research group. The goal is to investigate and asses the accuracy of the PvdW and PR models coupled with the the so-called conservation element and solution element (CESE) algorithm for simulating nonideal compressible fluid flows. The latter is a branch of fluid mechanics which studies the characteristic of dense va- pors, supercrtical flows, and compressible two-phase flows where the thermodynamics of the fluid differ substantially from that of the perfect gas. In fact, in particular thermo- dynamic conditions, the fluid flows may exhibit nonclassical gas-dynamic phenomena, e.g., expansion shock waves. The application of nonideal compressible fluid flows in industry is already widespread. They can be found in CO2power cycles, pharmaceu- tical processing, transport of fuels at high-speed, organic Rankine cycles for power conversion. Therefore, an accurate understanding of the complex physics behind fluid flows that differ substantially from that of the perfect gas would undoubtedly pave the way for introducing technological improvements in real-world applications. A number of research projects are actively ongoing for better understanding and modeling non- ideal compressible fluid flows and defining implications in terms of engineering design. However, as already shown several researchers, the accuracy of the thermodynamic model has a strong influence on the simulation of nonclassical phenomena, to the point that their presence can depend on the accuracy with which fluid model parameters are determined. Thus, a high-fidelity and highly accurate simulation of nonideal compress- ible fluid flows is of paramount importance and can provide considerable insights both for the study of nonideal thermodynamics and engineering design and optimization. In this projects, the CESE algorithm coupled with a the PvdV and PR thermodynamic model will be used. The subsonic inviscid flow over a NACA-0012 airfoil, the tran- sonic inviscid flow over a NACA-0012 airfoil and the Prandtl-Meyer expansion will be used for the assessment of the numerical accuracy and efficiency of the new solver for nonideal compressible fluid flows.

BAS/1/1663-01-01

Applied Mathematics and Computational Science; GPU programming; Compressible Flows

Implement new thermodynamic models in an existing, high preformat CESE code, port fundamental part of the solver to single and multiple GPU and solve some canonical but extremely important test problems to validate the solver

Applied Mathematics and Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Extreme Computing Research Center

3D/Inkjet Printed Cube Satellite Transparent Antenna Design

Academic Program: Electrical Engineering

Advanced Terahertz Source Generation using Plasmonic Photomixers

Academic Program: Electrical Engineering

Inkjet Printed Wireless Temperature and Humidity Sensors for Smart Bandage Application

Academic Program: Electrical Engineering

Inkjet Printed Flexible RF Energy Harvesting Module

Academic Program: Electrical Engineering

Dynamics of MEMS for mechanical computing

Academic Program: Mechanical Engineering

Design of hydraulic structures to reduce structural damages via multiphase simulations

Academic Program: Applied Mathematics and Computer Science

Iterative algorithms for scalar conservation laws and volume of fluids

Academic Program: Applied Mathematics and Computer Science

Improving conservative level-set methods for multiphase simulations

Academic Program: Applied Mathematics and Computer Science

This projects starts with a newly developed conservative level-setmethodfor multiphase flows. This method combines ideas from the level-set and the volume of fluid schemes into a monolithic model. The model contains a consistent term that regularizes the Jacobian of the non-linear equation and that penalizes deviations from the signed distance function.The result is a conservative level-set model that does notrequirereconstruction of the interface and that produces an approximation of the signeddistancefunction (to the fluids interface). In the current project we aim to improve the method in the following three fronts: - The current form of the method does not require extra stabilization of the advective term since it depends upon the penalization term. This however, implies that one can't reduce the influence of the penalization or instabilities might start to appear. We want to introduce extra and independent stabilization to the advective term. The model is a conservation law for a regularized Heaviside function. The reason for this is that integration of discontinuous functionsrequiresnon-standard methodologies within the context of finite elements.We plan to use state of the art integration techniques that would allow us to use exact Heaviside functions improving the conservation properties and overall quality of the solution. - The model contains a user defined parameter. To obtainqualitativelygood results one might need to select this parameter depending on the problem. This dependency can be mitigated via optimal control theory that would allow the algorithm to automatically select an optimal parameter for any given problem. We plan to test each modification to the method via a set of well established benchmarks in the area of multiphase flows.

BAS/1/1616-01-01

Applied mathematics and computational science

Implementation and testing of the proposed algorithm

Applied Mathematics and Computer Science

Computer, Electrical and Mathematical Sciences and Engineering

Modeling shallow water waves via CG-FEM with monolithic convex limiting

Academic Program: Applied Mathematics and Computer Science

Exact versus approximate Riemann solvers for finite volume methods

Academic Program: Applied Mathematics and Computer Science

A domain- specific language and code generation for Riemann solvers

Academic Program: Applied Mathematics and Computer Science

Laser-based Sensor Design and Development

Academic Program: Mechanical Engineering

Future Fuel for Advanced Combustion Engines

Academic Program: Chemical Science

Chemical Kinetics of Novel Biofuels

Academic Program: Mechanical Engineering

Theoretical and numerical study on complex materials

Academic Program: Applied Mathematics and Computer Science

Nonlinear Partial Differential Models

Academic Program: Applied Mathematics and Computer Science

Classification of long non-coding RNAs

Academic Program: Computer Science

Novel Pincer complexes for catalysis

Academic Program: Chemical Science

Role of non-classical hydrogen bonding in organocatalysis

Academic Program: Chemical Science

Frebonics- Nature inspired engineering

Academic Program: Electrical Engineering

Nature has amazing mysteries. The vast ecosystem is balanced in impeccable efficient manner. Take an example, butterfly. How do they choose flowers to fly around? How their wings are so colorful? How to they morph from a larva to a full blown butterfly? How do they fly? All these are essentially vast set of engineering: motor mechanics, materials and bits of devices here and there synchronized via physics and mathematics. In this project, we explore such engineering in nature and inspired by that learning we simply complex engineering to address global challenges and augment the quality of our life. We believe by integration of freeform electronics (physically flexible, stretchable and reconfigurable in shape and size) with robotics we can imagine and create various frebonics which can essentially be used for advanced healthcare like prosthetics, artificial organs, advanced environmental monitoring, security and self-driven vehicular technology. We are presently working on understanding how flowers bloom: from a tiny entity when it blossoms completely – imagine a display system like your 5.5” iPhone when stretched becomes a 55” television. Fusion of electrical engineering, mechanical engineering, bioengineering, chemistry, civil (structural) engineering, computer science, material science and engineering, we envision to make such a singular gadget which can be reconfigured adaptively in shape and size. This is just one example and there are many – we look forward to working with you!

BAS/1/1619-01-01

Electrical Engineering, Mechanical Engineering, Bio Engineering, Computer Science (Robotics, Automation), Civil Engineering (Structural Engineering), Physics, Material Science and Engineering

Literature survey, programing, fabrication, material and device analysis, device characterization, system integration, oral and written reports.

Electrical Engineering

Computer, Electrical and Mathematical Sciences and Engineering

DIY Electronics

Academic Program: Electrical Engineering

Power Shell Integrated energy network with flexible and stretchable solar cells, hydrogen generators and storage.

Academic Program: Electrical Engineering

More Than Natural Skin

Academic Program: Electrical Engineering

First-principles investigation of superlattices of 2D materials

Academic Program: Materials Science & Engineering

Novel Phenomena at perovskite interfaces and superlattices

Academic Program: Materials Science & Engineering

First principles modeling of hybrid organic-inorganic perovskites

Academic Program: Materials Science & Engineering

Deep Learning for Visual Computing

Academic Program: Computer Science

Computer Graphics, Computer Vision, and Visualization

Academic Program: Computer Science

Combining deep learning and ontologies

Academic Program: Computer Science

High-Speed Wireless Communication for Data Centers and High Performance Computing

Academic Program: Electrical Engineering

Study of Optical Wireless Communication Systems

Academic Program: Electrical Engineering

Study of Physical Layer Security Systems

Academic Program: Electrical Engineering

Study of Massive MIMO Systems for 5 G Networks

Academic Program: Electrical Engineering

Energy Harvesting-Based Wireless Sensor Networks

Academic Program: Electrical Engineering

Study of Cognitive Radio (Spectrum Sharing) Systems

Academic Program: Electrical Engineering

Development and optimization of algorithms for cloud-enabled heterogeneous radio-access networks

Academic Program: Electrical Engineering

Adhesion phenomena across interfaces with spatially heterogeneous adhesive properties

Academic Program: Mechanical Engineering

Ultra Sensitive Sensors based on Nanotubes Porous Networks

Academic Program: Mechanical Engineering

Experiments on High-Speed Multi-Phase Flow

Academic Program: Mechanical Engineering

Monitoring subsidence due to groundwater pumping in central Arabia

Academic Program: Earth Science and Engineering

Applications of reduced graphene oxide

Academic Program: Chemical Science

Wafer-scale patterned growth of vertically aligned carbon nanotubes

Academic Program: Materials Science & Engineering

Simulations of Large Scale Turbulent Flames using OpenFOAM

Academic Program: Mechanical Engineering

Simulations of Spray and Combustion in Engines

Academic Program: Mechanical Engineering

Direct Numerical Simulation of Turbulent Combustion at High Pressures

Academic Program: Mechanical Engineering

Data Assimilation into large dimensional systems

Academic Program: Earth Science and Engineering

Ocean Modelling and remote sensing the Red Sea circulation and ecosystem

Academic Program: Earth Science and Engineering

Enhancing Weather Downscaling and Forecasting

Academic Program: Earth Science and Engineering

Constraining Earth Fluid Motion Models with Satellite Images

Academic Program: Applied Mathematics and Computer Science

Quantifying and reducing uncertainties in earth fluid models

Academic Program: Earth Science and Engineering