Total Result(s) Found: 141

Role of repetitive elements in aging and related pathologies.
Academic Program: BioScience
About half of the mammalian genome is comprised of repetitive and mobile DNA elements. While their role in evolution and phenotype variation is well understood, their physiological function in the some remains largely unexplored. Recent work from our lab established an important epistatic role of L1 retrotransposable elements in driving the progression of pathological aging via chromatin remodeling (Della Valle et al Sci. Trans. Med. 2022). Complementary unpublished results from our lab indicate an unanticipated positive role of cytoplasmic L1 RNA also in tissue homeostasis and regeneration. The project will focus on the investigation on the mechanistic role of retrotransposon RNA mediated response in both epigenome and tissue repair plasticity, and the exploration of RNA based potential therapeutic strategies.
BAS/1/1037-01-01
amira.eltally@kaust.edu.sa
Epigenetics, Aging, Stress response, RNA Therapy,
Epigenetics, Noncoding RNA, Aging, Cell Therapy
Identification of L1 interactors involved in epigenome and tissue homeostasis Identification of drugs (RNA based) preventing stress response mediated senescence and cancer.
BioScience
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Vacuum deposition methods for perovskite-silicon tandem solar cells
Academic Program: Materials Science & Engineering

Perovskite-silicon tandem solar cells are a very promising technology for harvesting the energy of sunlight. Perovskite-silicon tandem solar cells can theoretically achieve power conversion efficiencies in excess of 30%, which is higher than that of single-junction perovskite or silicon solar cells.


This project concerns the development perovskite-silicon tandem solar cells based on double-side-textured c-Si wafers.  The student will work on the fabrication of such cells and the study of deposition methods that are scalable and conformal with a high degree of uniformity and reproducibility. The student will work with various vacuum based techniques, such as thermal evaporation and ALD, for the deposition hole-transport (HTL) and electron transport layers (ETL), with the aim of developing precursors for all-vacuum-evaporation deposition protocols of large scale and highly efficient tandem solar cells. 
BAS/1/1345-01-01
osman.bakr@kaust.edu.sa
Material Science
Material Science
  1. Fabrication of perovskite-silicon tandem solar cell devices.
  2. A systematic investigation of the effects on device performance of different deposition parameters for perovskite precursors, perovskite film morphology, and interphases between perovskite absorber and HTL/ETL.
  3. Stability testing of devices under various temperatures and light-soaking conditions.
Materials Science & Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
KAUST Solar Center
Seawater Reverse osmosis (SWRO) pretreatment impact on microbial growth potential
Academic Program: Environmental Science and Engineering
The desalination of seawater using reverse osmosis membranes (RO) is an attractive solution to global freshwater scarcity. However, membrane performance is reduced by (bio)fouling. Considering that (i) the global production of desalinated seawater by RO is at 65.5 million m3/d, (ii) a membrane single membrane production capacity of 12 m3/d, and (iii) an average membrane replacement rate of ~10% to 15% per year due to fouling, then about 825000 membranes go to waste every year worldwide. Among the fouling types, biofouling –membrane deposition of bacterial cells and subsequent microbial growth– is the most difficult to alleviate. The lifetime of RO membranes would be extended if seawater pretreatment units, prior to RO, efficiently removed the material causing biofouling. The problem with pretreatment in desalination plants is that we lack a robust biological-based method to assess their efficiency to remove biodegradable nutrients and microbial cells. Current methods to assess the quality of seawater entering the RO such as turbidity and silt density index do not inform the water’s microbial growth or biofouling potential; hence the performance of the receiving RO membrane is jeopardized.
FCC/1/1971-42-01
luca.fortunato@kaust.edu.sa
Desalination; biofilm; biofouling; Reverse Osmosis;
Environmental science and engineering
This research will develop a biological-based monitoring system based on microbial and biofilm growth potential to determine the efficiency of filtration pretreatment processes. The idea is to develop and implement a sensitive method to assess the microbial and biofilm growth potential SWRO pretreatment units.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
ClO2 for biofouling control in Seawater Reverse Osmosis
Academic Program: Environmental Science and Engineering
It is desirable to apply a disinfectant for biofouling control in the membrane elements as well as in the intake system of seawater reverse osmosis (SWRO) plants. Chlorine dioxide (ClO2) would be an ideal candidate, since it is a highly effective disinfectant, does not produce chlorinated disinfection by-products, and as a dissolved gas it easily passes the membrane, allowing disinfection of the permeate side. Compared to chlorine and chloramine, ClO2 is a less aggressive oxidant, however, it has a higher oxidizing capacity. Polyamide membranes are known to be easily damaged by oxidants, and the potential for membrane damage is hampering the application of ClO2 in reverse osmosis. The presence of bromine, relatively high pH and high temperature, suggests that in Middle East, it is a serious possibility that membrane damage occurs in SWRO. Conversely, it is highly likely that ClO2 dosing is effective against biofouling, since it is an effective disinfectant and it can easily be transported into the biofilm and through the membrane.
FCC/1/1971-42-01
luca.fortunato@kaust.edu.sa
Desalination; Chlorination; biofouling; disinfection;
Environmental science and engineering
The main objective is to evaluate the potential membrane damage due to ClO2, and the second objective is to evaluate the effect of ClO2 on fouling.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
Breaking the Vehicle Over-The-Air Update System
Academic Program: Computer Science
A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and vulnerabilities, which can lead to catastrophic failures that threatens human lives. This makes the manufacturers liable to such failures and thus often caused millions of vehicles recalls for repair. A smart solution is to take advantage of the vehicle connectivity to the Internet and surrounding and perform Over-The-Air (OTA) software and firmware when needed, very similar to smart phone software updates. It is clear that this process is critical and can have negative consequences if the OTA update system unreliable and insecure. We have introduced an OTA protocol and corresponding Proof of Concept (PoC) implementation that ensure an end-to-end chain of trust between all stakeholders: the manufacturer, suppliers, brokers, and the vehicle.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
The Update Framework, Chain-of-Trust, Security, Over the Air software/firmware updates.
Connected Vehicles, Autonomous Vehicles, Software updates, Over-the-Air (OTA), security
The goal of this project is to demonstrate some attacks by running the PoC on embedded devices or even in a real vehicle. The role of the intern will be to understand the system and extend the demos we have already done in software, and experiment them empirically on real relevant devices. The objectives are to (1) raise awareness to the consequences of not doing OTA updates right, (2) to gauge if our system is secure empirically (3), and to improve it if is not.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Vehicle Intrusion Resilience Systems in Action
Academic Program: Computer Science
A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and intrusions, which can lead to catastrophic failures that threatens human lives. A Fault and Intrusion Resilient System (FIRS) is a vehicle middleware that can mask the effect of a failure or intrusion. Contrary to Intrusion Detection and Protection Systems, FIRS ensures the continuation of the function despite intrusions. FIRS works as follows: it allows an application to run different replicas on different ECUs simultaneously. For each function executed by the application, an agreement is collected from a majority of ECUs through the (in-vehicle) network, and the corresponding output is returned. As long as the majority is not compromised, the integrity of the returned output is guaranteed despite the existence of faults or intrusions in the rest of ECUs. We have an implementation of a FIRS protocol that we are experimenting on Omnet++ simulator.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
Intrusion Resilience Systems (IRS) for modern vehicles, CAN Vehicular networks,
Intrusion Resilience, Intrusion detection and prevention, Vehicular networks, CAN, Byzantine Fault Tolerance
The goal of this project is to create a demo that validates the FIRS on a real hardware and software. The intern will build a small testbed of networked embedded devices, e.g., RaspberryPIs or ECUs. Two network types are of particular importance: (1) the widely used broadcast-based Control Area Network (CAN), can be built using RaspberryPIs and CAN transceivers; and (2) the more recent efficient Ethernet for Automotive that, as the name indicates, has similarities to the Ethernet protocols in IT networks. The objectives of the work are to understand how FIRS behaves empirically, build the small testbed for validation, and demonstrate the work in a sub-real environment.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Rejuvenation of Diverse FPGA Softcores in a SoC
Academic Program: Computer Science
A field-programmable gate array (FPGA) is an integrated circuit designed to be reconfigured by the user after manufacturing to build a System-on-Chip (SoC) embedded device. The needed logic is usually implemented as a software image and then instantiated on the FPGA to inherit the nice properties of hardware, like higher speed and better security. Unfortunately, since the image itself, e.g., a Softcore that represents a Processing Unit, is a software, it is prone to faults and vulnerabilities that manifest after instantiation on the FPGA. Unfortunately, an Advanced Persistent Threat (APT) is possible if a determined adversary managed to discover a new vulnerability to initiate a zero-day, leaving no chance for classical detection and prevention tools to recover. In addition, the softcore can include bugs and glitches that manifest only at run time. Fault and Intrusion Tolerance (FIT) is a technique used to make a process resilient to such attacks by masking them. A FIT protocol replicates the processors, i.e., a softcore in our case, by running three versions simultaneously, and collecting a majority agreement (or consensus) on each operation. If the majority (e.g., 2/3 processors) did not fail at the same instant, the fault is masked, and the SoC continues operation as designed. This requires some level of diversity in the running softcore to increase the chances of independence of failures.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
FPGA, Microblaze, RISC-V, Openpiton, Fault and Intrusion Tolerance
FPGA, System on Chip, Replication
The goal of this project is to experiment running an FIT we are implementing on a diverse softcores, e.g., Microblaze, RISC-V, Openpiton, etc., on an FPGA and simulate some fault or attacks. We are experimenting the concept on a Xilinx Zinc board using equivalent replicas. The objectives are to check the feasibility of running the FIT with different softcore types and evaluate the behavior in action. The intern will acquire all this knowledge and publish the results by working with a team of experts.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Useful Bitcoin Mining with a Matrix-based Puzzle
Academic Program: Computer Science
Cryptocurrency and blockchain technologies are increasingly gaining adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain’s security, asset generation, and maintenance. In most cryptocurrencies, and mainly Bitcoin, the “work” a miner must do is to solve a cryptographic puzzle: to find a random nonce that once (cryptographically) SHA-256 hashed with a perspective block header, returns a 32 Bytes number having a leading pre-defined number of zeros (called difficulty). This puzzle represents the PoW, and lives forever in the blockchain (together with the block), allowing for future verifications. The main property of this puzzle is being very hard to solve, but easy to verify. Unfortunately, solving the puzzle is a very controversial being computation-hungry process that manifests in very high energy consumption (e.g., similar to the total electricity consumption of Denmark in 2020). Although other environment-friendly solutions are being suggested, e.g., Proof of Stake, the Bitcoin community has no plans to change the mining method using cryptographic puzzle. Shutting done the Bitcoin network is not an option either because it can disrupt the global economy with a market cap of around half trillion dollars as per today. Proof of eXercise (PoX) is an alternative puzzle that is getting more acceptance in academia. PoX suggests a matrix-based puzzle, e.g., matrix product and determinant calculation, that has close security properties to the cryptographic puzzle, but has at the same time useful benefits for the community, e.g., DNA and RNA sequencing, protein structure analysis, im-age processing, data mining [16], computational geometry, surface matching, space model analysis, etc. While computing the matrix product is very hard (which is required by design), its verification is also hard, making it infeasible. PoX proposes a probabilistic verification protocol that challenges the miner to only give the results of selected columns x rows multiplication for verification, thus making verification easy. Since selection is random, the miner cannot guess the columns x rows apriori, and thus must have computed the matrix correctly.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
Bitcoin, Proof of eXercise, Bitcoin mining, Matrix product
Supercomputing, Blockchain, Cryptocurrency
The goal of the project is to experiment the feasibility of this matrix-based puzzle empirically on the Sheheen super computer at KAUST. The intern will work with RC3 experts and Shaheen engineers to realize the experiments. The objectives of the project are to present to the community evidences that the proposed PoX puzzle is a reasonable alternative to the cryptographic puzzle from the security and monetization perspectives (e.g., the miner has more incentives since it gets paid by the problem proponent as well and by getting the coins while mining). The results of the project will be published or commercialized.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Advanced Breach and Attack Simulation using ML
Academic Program: Computer Science
Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these tools can be using scripts of known attack vectors, and running them in sequence. This, however, does not cover unknown zero-day attacks. An intelligent way would be to try to account for potential attack that are unknown. We envision that using some Machine Learning techniques trained on some types of vulnerabilities can make this automation smarter.
BAS/1/1696-01-01
ali.shoker@kaust.edu.sa
Cybersecurity, Machine Learning, Deep Learning, Breach and Attack Simulation,
Cybersecurity, Machine Learning, Deep Learning, Penetration testing
The goal of this project is to experiment the use of Deep Learning or Generative Adversarial Networks as a tool to optimize the Breach and Attack Simulation. The intern will make use off-the-shelf tools that follow the same method for the detection of critical faults, e.g., memory overflows, and extend it for more security vulnerabilities (e.g., network). The objectives of the project are to understand the feasibility of ML model in optimizing BAS tools and publish the results as a paper or commercialize the project.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Achieving sustainable urban greening through the integration of anaerobic membrane bioreactor and nature-based biofiltration landscaping gardens.
Academic Program: Environmental Science and Engineering
The anaerobic membrane bioreactor is able to clean municipal wastewater at potentially lower energy costs than the conventional aerobic treatment processes 6. Anaerobic microorganisms within the membrane bioreactor do not remove ammonia and phosphate, hence resulting in a final effluent quality that can serve as liquid fertilizers for the landscaping plants and trees. Nature-based biofiltration system is a sand filtration unit that will consist of specifically layered filter media with saturated zone at the bottom, and planted with local plants and trees that are effective in pollutant and nutrient removal. Additional physical removal of contaminants is further achieved as the water flow through the filter media. Clean treated wastewater is collected at the drainage layer and can be reused for other purposes. By integrating the anaerobic membrane bioreactor-based wastewater treatment plant with the nature-based biofiltration landscaping feature, we can achieve the following: - Near zero-energy cost in treating municipal wastewater - Landscaping features with minimal water loss - Recovery of clean water that can be reused for other purposes
BAS/1/1033-01-01
peiying.hong@kaust.edu.sa
Nature-based solutions, Biofiltration
Environmental science and engineering
- Operate a high-throughput column experiments to determine how different variables (SandX, biochar, type of wastewater, plants, emerging contaminants) can affect the nature-based biofiltration columns - Monitor water quality - Monitor microorganisms
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
Mathematical Modelling of Neuronal Morphodynamics.
Academic Program: BioEngineering
Computer simulations of cellular behaviour of single neurons or small networks of neurons with an accurate dendritic and axonal morphology have become increasingly common. The complex morphology of the neurons is usually taken from experimental data resulting in anatomically precise compartmental models. The complex geometric morphology is important for the understanding of the neuronal integration, learning and memory. In the project we want to develop a mathematical model for the growth and branching of neurons in the cortex. This model will be based on differential equations and describe the growth dynamics of neurons.
BAS/1/1674-01-01
gabriel.wittum@kaust.edu.sa
modelling and simulation in neuroscience, neuronal morphodynamics
Applied Mathematics and Systems Biology
- Model - Implementation and computation - draft of a manuscript for publication
BioEngineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Extreme Computing Research Center
The KSA Native Genome Project
Academic Program: Plant Science
Environmental protection via the conservation of biological diversity is a central theme of the Kingdom of Saudi Arabia’s (KSA) Vision 2030. Biologically characterizing ecosystems that are relevant to the mission of MEWA’s National Center for the Development of Vegetation Cover & Combating Desertification (NCDVC&CD) would provide essential data as to how and why a particular ecosystem operates in a particular environment. This information can then, in turn, be used to engineer large-scale ecosystems that are sustainable, will positively influence vegetation cover, and will fight against desertification across the KSA. In partnership with the NCDVC&CD, the CDA are studying many common and endangered plant species of cultural, religious, medicinal and pastoral significance. This project will establish a robust pipeline/infrastructure to characterize 100s-1000s of different ecosystems across the Kingdom, both terrestrial and aquatic, as part of a broader initiative entitled the KSA Native Genome Project (KSA-NGP) under development. Furthermore, it will produce a new cohort of scientists trained in the study of ecosystem biology and its application to environmental protection and conservation.
FCS/1/4516-01-01
nahed.mohammed@kaust.edu.sa
Plant Biology, Genomics, Environmental protection, Conservation, Biodiversity, Sustainable Agriculture, food security
Genomics, Plant biology, Bioinformatics, Molecular Biology
- Generate high-quality genomic resources for all native plants under study, and associated microbial species in the KSA - Investigate their ecosystems with respect to metabolomes, root development/architecture, and microbiomes
Plant Science
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Center for Desert Agriculture
Biodiversity of Red Sea Reef Fishes
Academic Program: Marine Science
Documenting reef fish communities through surveys and collections across spatial and temporal scales is key to understanding changes of biodiversity across natural environmental gradients and local habitats, or to predict future trajectories in response to local and global stressors. Using a range of sampling and survey methods, we are building a comprehensive picture of fish communities within the Red Sea. Work will focus on the documenting fish communities in reef and non-reef habitats (e.g., mangrove, seagrass, macroalgae) to understand the connection and importance of habitats to ecosystem function and services. This focuses on conspicuous and cryptic fishes with opportunities to conduct field (diving, fish surveys and collections) and lab work (dissections, genetic barcoding).
BAS/1/1010-01-01
​​Biodiversity, Ecology, Marine Science
​Depending on the results, the student researcher may also contribute to a scientific publication detailing the work and findings​.
Marine Science
Biological and Environmental Sciences and Engineering
Red Sea Research Center
Connectivity of Shark Populations
Academic Program: Marine Science
​Ongoing research conducted by the Reef Ecology Lab and its collaborators have generated a diverse collection of shark and ray genetic samples. Analysis of these samples could provide insight into both elasmobranch biogeography and population ecology. Specific projects include investigating broad population genetics of hammerhead sharks in the Red Sea and Arabian Gulf or looking at fine-scale differences between reef shark sub-populations within the Red Sea. Undergraduate research will mostly focus on lab work, performing DNA extractions, running PCRs, and analyzing our existing collection of genetic samples. There may also be opportunities for fieldwork, including fish market surveys and catch-and-release sampling trips.​
BAS/1/1010-01-01
​Population Genetics
​Depending on the results, the student researcher may also contribute to a scientific publication detailing the work and findings. 
Marine Science
Biological and Environmental Sciences and Engineering
Red Sea Research Center
Integrating microbial electrolysis cell with anaerobic digestion to enhance resource recovery from waste activated sludge
Academic Program: Environmental Science and Engineering
Anaerobic digestion (AD) is a well-established biotechnology for generating methane from high-strength organic wastes such as waste activated sludge (WAS). However, several bottlenecks exist that hinder their widespread application such as low start-up time, low methane content and yield, and high susceptibility to environmental perturbation. One strategy to alleviate these bottlenecks is to integrate AD with microbial electrolysis cell (MEC). In MEC, acetoclastic electroactive bacteria (EAB) are considered the key functional groups responsible for recovering energy from acetate. In this project, we will study the effect of different start-up strategies with functionally redundant and efficient acetoclastic EAB on the performance of integrated MEC-AD fed with WAS.
BAS/1/1021-01-01
hari.anandarao@kaust.edu.sa
microbial electrolysis cell; anaerobic digestion; waste activated sludge
environmental science and engineering
Develop start-up strategies to enhance performance of MEC-AD
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
Forensics analysis of the malicious bot scrapers ecosystem
Academic Program: Computer Science
Web scraping bots are now using so-called RESidential IP Proxy (RESIP) services to defeat state-of-the-art commercial bot countermeasures RESIP providers promise their customers to give them access to tens of millions of residential IP addresses, which belong to legitimate users. They dramatically complicate the task of the existing anti-bot solutions and give the upper hand to the malicious actors. We have developed a new technique to detect traffic coming through such proxy and, in collaboration with industrial partners, have gathered a very large datasets of such connections, and measures thereof. In this project, we want to analyse that dataset according to various view points and, in particular, we want to investigate whether it is possible to use a new multilateration algorithm that we have developed to geolocalize the malicious actors hidden behind the proxies. If successfull, this would immensely benefit the good actors trying to protect the scraped websites. This work will require strong analytical skills, rigorous mindsets and creativity. The intern will have to try to extract intelligence information from a large dataset. A desire to acquire hands on experience with big data analytics (most likely SQL based) as well with visualization techniques is a must. Python programming will most likely be required.
BAS/1/1697-01-01
marc.dacier@kaust.edu.sa
scraper websecurity cybersecurity bots
web security
a platform to systematically analyse large amount of data provided to the intern must be built. It will offer a visualisation of the intelligence extracted from the data by the intern. If successful, this could lead to a scientific paper to be written for a conference dealing with security visualisation techniques.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Security Training in/with/for the meta verse
Academic Program: Computer Science
The goal of this project is to assess the feasibility of using virtual reality for security training. Without going into the details, suffice it to say that we want to build something equivalent to a "Capture the Flag" (CTF) experience in VR. That CTF could be played by players having different levels of expertise. This could be a very simplified view of the world where the user would have "super power" to run attacks, eg in a smart city. It could also be played by an experience person to run detailed attacks against, eg PLCs. In both cases, the underlying system under test would remain the same. In the context of this project, we have limited ambitions and want to build a first proof of concept to assess the feasibility of developing such environment in VR (eg using an oculus). The development of the parts specific to the VR will be done in collaboration with an engineer who has a long expertise. The intern task will mostly be devoted to the CTF aspect, the networking elements in particular. It will be carried out in collaboration with other people. A real interest for networking security is a must, as well as a good understanding of network protocols and python programming.
BAS/1/1697-01-01
marc.dacier@kaust.edu.sa
network security cybersecurity virtual reality vr pentest
Penetration testing and virtual reality
A proof of concept running in an oculus of a simple CTF playable in a virtual environment. If successful, we would like to also share the lessons learned while doing this work in a scientific conference devoted to teaching and training cybersecurity. A study of the state of the art would thus be required and a paper written at the end of the project with the results obtained
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Pen testing the intra network elements
Academic Program: Computer Science
A number of elements exist in the network that can affect the quality, reliability, performance of network connections. They are normally used to improve the end user experience but remain mostly invisible to him/her. Examples of such elements are Web Access Firewall (WAF), Network Firewalls, Traffic shaper, Intrusion Prevention Systems (IPS), Proxies, CDNs, Tunnels, Encapsulation/Translation mechanisms (IPV4/IPV6, HTTP1/HTTP2, etc.), etc.. It is thus very important to continuously verify that these systems behave as they should, that they have not been misconfigured (accidentally or intentionally). It is also very important to be able to verify that no malicious actor has introduced such element on a route between two communicating parties. As part of an ongoing research project, we have developed a platform that enables to generate test cases and test campaigns exactly for that purpose. The goal of this project is to use that platform to develop test campaigns against specific use cases, such as the detection of a WAF, for instance. The campaigns, once produced, will be tested experimentally at large scale by using machines deployed all over the world. The analysis of the results and of the lessons learned is going to be part of the project as well.
BAS/1/1697-01-01
marc.dacier@kaust.edu.sa
network security cybersecurity firewall waf experiments
network security
The intern, together with the other people involved in this project, will first select an interesting use case and, then, develop the test campaigns needed for that target. He/she will design an experimental campaign and run it. He/she will analyse the experimental results. The ultimate goal will be to produce a paper summarizing the work that could be submitted to a security or networking measurement conference. A desire to understand how networks function, an appetite for looking at packets and strange protocols is a must. Python programming is going to be required.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Egaming security
Academic Program: Computer Science
Academic Program: Electrical Engineering
Online Gaming represents a huge economic sector these days, even bigger than the movies industry. They enjoy a spectacular and continuous growth. They rely on a number of different business models but, in many games (not to say most of them), there are financial incentives for players to cheat to improve their performances, to attack others to win parties, to rely on external third parties to acquire new privileges/skills, to buy/sell virtual elements of the games against real monetary values, to misuse the games to commit money laundering, etc… Over the last years, a great creativity has taken place among fraudsters to provide techniques, tools, services to cheat and commit fraud. The goal of this project is twofold. First, we will carry out a review of the state of the art in terms of scientific contributions to the field. Second, we will experiment with real attacks, in a confined environment, on real platforms, assess their severity and find ways to mitigate them. Any new attack found in the course of this work will be reported to companies following a responsible disclosure process. You do not need to be an experienced gamer to apply for this project but if you are, it could help. Most importantly, a desire to understand how networks function, an appetite for looking at packets and strange protocols is a must. Python programming is going to be required. Particular attention will be devoted to ethical consideration before experimenting with any of the identified cheating techniques. Any student misusing the knowledge gained during this project, for his own profit or others, will suffer severe consequences
BAS/1/1697-01-01
marc.dacier@kaust.edu.sa
cybersecurity networking security esport egaming
Networking security
As part of the SeRBER team, the intern will produce a review of the state of the art,. He/She will build a confined networking environment amenable to run repeatable experiments with various games and various game platforms and launch different kinds of attacks against them, while measuring various characteristics of the attack. Mitigation techniques will also be experimented with.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Resilient Computing and Cybersecurity Center
Tackling the challenges of NO-Laser Induced Fluorescence technique in hydrogen detonation
Academic Program: Mechanical Engineering
Compared to classical constant volume or constant pressure thermodynamic cycles, the detonation regime of combustion could increase by 40% the efficiency of engines. In line with the Paris agreement, identifying more efficient combustion processes is one of the strategies to limit CO2 emissions that contribute to climate change. For transportation, researchers focus on obtaining and controlling a self-sustained detonation in a specific engine (PDE or RDE). While the measurement of temperature and chemical species is of current practice in conventional combustion process (flames, engines, etc…), the experimental characterization of detonation relies on the determination of the detonation velocity, global pressure, and density gradient structure. These information are limited to validate numerical simulations and to be confident in the phenomenological comprehension extracted from it. While planar laser-induced fluorescence of hydroxyl radical (NO-PLIF) is a powerful technique to characterize reaction fronts, previous studies have shown significant limitations of this technique for detonation visualization. Not only restricted to reaction front visualization, this technique is also of interest as it can give access to 2-D temperature measurements in detonations. Objectives: The main objective of the project is to overcome the current limitations of the NO-PLIF imaging of detonation. This numerical investigation is based on a preexisting PLIF model that will be used (i) to identify the sensitive parameters (excitation line, laser energy, gas composition, etc…) of the PLIF intensity and (ii) to recommend experimental conditions to maximize the overall image quality.
BAS/1/1396-01-01
mhedine.alicherif@kaust.edu.sa
Detonation front; Laser diagnostics; Numerical simulations; Spectroscopic analysis
Combustion; shock waves
First, the student will have to become familiar with the principle of the NO-PLIF technique and the particularities associated with its usage on detonations, which has high pressure and temperature variations, high-speed flow (up to 2000m/s), etc… Second, the sensitivity analysis of the fluorescence signal will be conducted to identify the most sensitive parameters of the PLIF signal. Third, optimal operating conditions (≠ maximizing the fluorescence signal) will be identified and tested experimentally. Due to both the strong non-linearities between the PLIF signal intensity and each parameter involved, machine-learning approaches may be used to facilitate the identification of the optimal operating conditions.
Mechanical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Clean Combustion Research Center
Scaling Graph Neural Networks to 1000s of GPUs
Academic Program: Computer Science
Graph Neural Networks (GNNs) are a special type of deep neural networks that deal with graphs, instead of the more traditional images. GNNs are used in a variety of applications, from recommendation systems, to social networks, to computer security, to biological networks. The common characteristic is that graphs tend to be large and complex; therefore both training and inference require significant processing power. The goal of this project is to scale GNN training to thousands of GPUs. We will target our new supercomputer, Shaheen III, which is projected to include 2800 Nvidia Hopper super-chips than combine a CPU with a H100 GPU https://www.nextplatform.com/2022/09/26/kaust-hpe-shaheen-iii-supercomputer We will use the latest frameworks, such as Microsoft DeepSpeed, and we will target very large graphs.
BAS/1/1603-01-01
panos.kalnis@kaust.edu.sa
Deep Neural Networks; DNN; Graph Neural Networks; GNN; High Performace Computing; HPC
Machine Learning
- Tensorflow or PyTorch - based implementation - Project report
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Extreme Computing Research Center
Statistical and machine learning methods for health and environmental applications.
Academic Program: Statistics
The student will work on the development of statistical and machine learning methods for health and environmental applications. The topic is flexible and potential research areas include disease mapping, early detection of disease outbreaks, air pollution modeling, forest fires prediction, integration of misaligned spatial and spatio-temporal data, and the development of R packages for data analysis and visualization. Examples of research projects can be found at https://www.paulamoraga.com/research
BAS/1/1693-01-01
paula.moraga@kaust.edu.sa
statistics, mathematics, computer science
statistics, mathematics, computer science
The student will work on the development of statistical and machine learning methods for health and environmental applications. The topic is flexible and potential research areas include disease mapping, early detection of disease outbreaks, air pollution modeling, forest fires prediction, integration of misaligned spatial and spatio-temporal data, and the development of R packages for data analysis and visualization. Examples of research projects can be found at https://www.paulamoraga.com/research
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Egocentric Video Understanding
Academic Program: Computer Science
Have you ever imagined having a robot that cooks every meal for you? Have you ever dreamt about experiencing a different world in the Metaverse? Have you ever expected to have your glasses tell you where you left your keys and how to navigate to your favorite restaurant step by step? If yes, egocentric video understanding is what can lead you there. Egocentric videos are videos recorded from the first-person point of view, with the camera mounted on the head (e.g., GoPro) or smart glasses worn alongside the eyes (e.g., Google glasses), and they are what you actually see in your eyes. We need the AI system to automatically analyze and understand this type of videos to achieve the goals mentioned above. There are two key aspects in this problem: 1) large egocentric video data to fuel AI solutions; 2) effective techniques to generate correct predictions. For the first aspect, our IVUL group has devoted two years’ effort, together with 12 other universities as well as Meta (formerly Facebook), to achieve the largest egocentric video dataset called Ego4D. It contains 3000+ hours of egocentric video, spanning hundreds of scenarios captured by nearly 1000 unique camera wearers. Ego4D also defines various research tasks for egocentric video understanding, ranging from querying past memory, interpreting current behaviors, and forecasting future tendencies. For example, given a sentence “where and when did I last see my keys?”, the AI system returns the most recent video clip showing where your keys are. Or, the AI system automatically summarizes the video by telling you who is talking and what is his/her main point. Or the AI system predicts where you are walking to and what you are doing in the following minutes or even hours. For the second aspect, though Ego4D contains baseline solutions to each task, these solutions are far from practical for real-world application. There are two main challenges here. First, current solutions adopt techniques from video understanding tasks for third-person videos (where activities are recorded from a “spectator” view), which are dramatically different from egocentric videos in terms of recording perspective, camera motion, video continuity, etc. As a consequence, representations learned from third-person videos are not optimal to represent egocentric videos. We need to investigate novel feature representations specific to egocentric videos, or explore ways to smartly transfer the knowledge from third-person videos to egocentric videos. Second, egocentric videos pose new challenges for conventional methods due to their characteristics, such as noisy head motion, long videos and fragment actions. We need to address these challenges and improve the performance with novel techniques. In a nutshell, Ego4D is putting an apron on the robot and knocking on the door of the Metaverse, while at the same time, it is unveiling fresh challenges, which AI researchers are the key. It’s time to hop on board and contribute to this grand effort!
BAS/1/1653-01-01
bernard.ghanem@kaust.edu.sa
egocentric video; video understanding; computer vision;
Computer Vision; Machine Learning
(i) Effective feature representations of egocentric videos that benefit downstream tasks of egocentric videos, such as episodic memory, future anticipation; (ii) Novel techniques to transfer/translate between egocentric videos and exocentric videos; (iii) Improvement to retrieve ‘moments’ from past videos using a category, sentence or an object; (iv) Improvement to identify speaking faces in an egocentric video and summarize the speech.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Visual Computing Center
Next Generation Continual Learning
Academic Program: Computer Science
One of the most impressive abilities of human beings is the incremental learning ability. Since birth, we are always learning continuously without forgetting previously acquired knowledge that is useful for us. Could you imagine the amount of real-world applications that could be developed if we extended this human ability to modern-day AI systems (especially deep neural networks)? Modern deep neural networks adapt their parameters based on large-scale datasets to achieve state-of-the-art performance on specific computer vision tasks. However, due to legal and technical constraints and huge label diversity, deep learning models in real-world scenarios would rarely be trained just once time. Instead, they could be trained sequentially in several disjoint computer vision tasks without considering the data from previous tasks (because it may no longer be available for example). Therefore, these networks should learn incrementally without forgetting the previously learned knowledge. This is known as Continual Learning (CL). Currently, there are two families of CL methods. The first is rehearsal or memory-based methods, which select and store the most relevant samples to remember the current task when the following tasks are learned. The second group involves regularized methods that penalize changes to the most relevant parameters for the previous tasks. While the main studied challenge in the literature is learning with the least amount of forgetting on previous tasks, there are several other unexplored factors that affect learning from a stream of data. For instance, How fast is the learner in adapting the parameters of the model when receiving a new batch of data? If the learner is too slow (expensive training routine), then samples from the stream could be missed and not trained on. Thus, how can we benchmark different continual learning methods under budget constraint training? Furthermore, most continual learning benchmarks are focusing solely on the image domain leaving the more challenging video data unstudied. In the video domain, one of the main issues has been the lack of realistic, challenging, and standardized evaluation setups, making direct comparisons hard to establish. Therefore, our group IVUL has developed vCLIMB, a novel video class incremental learning benchmark, to promote and facilitate research on continual learning in the video domain. Video CL comes with unique challenges. (1) Memory-based methods developed in the image domain are not scalable to store full-resolution videos, so novel methods are needed to select representative frames to store in memory. (2) Untrimmed videos have background frames that contain less helpful information, thus making the selection process more challenging. (3) The temporal information is unique to video data, and both memory-based and regularization-based methods need to mitigate forgetting while also integrating key information from this temporal dimension.
BAS/1/1653-01-01
bernard.ghanem@kaust.edu.sa
continual learning; online learning
machine learning; computer vision
(i) A novel memory sampling strategy that learns to select a different number of relevant frames per video to reduce memory consumption while the performance remains almost the same; (ii) Novel training techniques/schemes to reduce forgetting. (iii) Benchmarking different continual learning methods on more practical metrics.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Visual Computing Center
Graph Neural Networks for Science and Engineering
Academic Program: Computer Science
Lots of data in scientific and engineering applications come with natural graph structures such as molecules in Chemistry, proteins in Biology, particles in Physics, planets in Astronomy, Bulk and MOF materials in Material Science, social and citation networks in Data Science, point clouds and meshes in Computer Vision and Graphics and so on. To model the information of such objects with complex structures, Graph Machine Learning, especially Graph Neural Networks (GNNs), has been proven as one of the most promising tools. Graph neural networks are deep learning architectures that can be trained to represent graphs with node features and edge features. For example, a molecule can be represented by a graph, where each atom is a node in the graph, and each bond is an edge. The atom numbers and types of chemical bonds are the associated node and edge features respectively. A GNN model can be trained to predict the quantum properties by learning on density functional theory (DFT) datasets which has huge potential to advance scientific discovery. Our group at IVUL have developed methods for graphs in 3D vision, videos, data mining, and fundamental science. We have developed GNNs with more than 100 layers with DeepGCNs (ICCV’2019, TPAMI’2021), and PU-GCN (CVPR’2021) for 3D point clouds segmentation and generation, G-TAD (CVPR'2020), VLG-Net (ICCVW’2021), MAAS (ICCV’2021) and VSGN (ICCV’2021) for large-scale video understanding, and DeeperGCN (arXiv’2020), 1000-layer GNN (ICML’2021) and FLAG (CVPR'2022) for node, link and graph level property prediction on Open Graph Benchmark (OGB) datasets which have graphs span nature, society and information domains. Are you excited about working on complex graph-structured data to make advances in biology, chemistry, physics, computer science, and so on? Would you like to use artificial intelligence to make fast predictions about the 3D structure of molecules, thereby speeding up the drug discovery process? Are you motivated by applications to precision medicine, and would like to create AI that learns to recommend what specific drug is suitable for a particular patient? Or perhaps you are more interested in higher-level abstractions, and would like to build an AI-based partial differential equation solver. All of these complex problems can be modeled through graph-structured data, and research in Graph Neural Networks can bring us closer to solving them. GNN has untapped potential in tackling graph based problems in Science and Engineering. However, more work is needed to explore the unique challenges to each scientific domain. In this project, you will have the chance to learn how to build large-scale graph neural networks and apply them to scientific and engineering applications.
BAS/1/1653-01-01
bernard.ghanem@kaust.edu.sa
graph neural networks
machine learning, AI for science
(i) Identifying the ground challenges of graph based problems in Science and Engineering; (ii) Collecting or processing the desired data into graph formats; (iii) Proposing novel GNN architectures and training techniques to tackle the challenges of learning on these graph data; (iv) Training and evaluating the proposed methods on specific metrics; (v) Producing well-performing and reproducible results and releasing the modular and reusable codebase to the research community.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Visual Computing Center
Next Generation 3D Understanding
Academic Program: All Programs
Academic Program: Computer Science
We are living in a 3D world. A broad range of critical applications such as autonomous driving, augmented reality, robotics, medical imaging, and drug discover rely on accurate representation of the three-dimensional data. While enormous efforts have been devoted to images and languages processing, it is still at an early stage to apply deep learning to 3D, despite their great research values. Here, we launch the project of next generation 3D understanding, and want to appeal more young talents like you to make this happen. We are particularly interested in two topics of the next generation 3D understanding: 1) a large-scale pretrained foundation 3D understanding model; 2) a vision generalist model that connects 2D and 3D vision data such as images, point clouds, and RGB-D. For the first topic, you might have heard that the trillion-parameter AI language model Switch Transformer by Google Brain excels across nature language processing tasks, and you might also know about the recent model Imagen with over 2 billion parameters can produce Photorealistic images from texts. Both of them are great examples of the power of large-scale models. Unfortunately, in 3D understanding, even the largest well-known network is still with less than 100 million parameters. How to increase the scale of 3D models in order to further unveil the power of deep learning in 3D application is a promising research direction. For the second topic, as a human, we can understand vision data despite its modality (2D or 3D). It is a step towards general artificial intelligence in computer vision to propose a single model that is able to have all knowledge about vision including 2D (images, videos), 3D (point clouds, RGB-D). This is an interesting topic but is under explored in the community. Our group at IVUL has put tremendous efforts and gained significant achievements in both topics. For the large-scale pretrained foundation model, our group is the first in the world that successfully trained a model with over 100 layers that achieved state-of-the-art performance in 2019 (DeepGCNs-ICCV19’). We broke our own record to 1000 layers in 2021 (GNN1000-ICML21’). Recently, we also propose scalable 3D networks with high inference speed in 2020s (ASSANet-NeurIPS21’, PointNeXt-arXiv22’). For the vision generalist model, our group has published impactful papers that involve understanding both view-images and point clouds (MVTN-ICCV21’, VointCloud-arXiv21’). Moreover, we have multiple ongoing projects in both directions. If you want to become a part in next generation 3D understanding, do not hesitate to join this project and achieve more with us!
BAS/1/1653-01-01
bernard.ghanem@kaust.edu.sa
3D computer vision
3D computer vision
(i) proposing a new large-scale foundation model for 3D understanding; (ii) proposing self supervision techniques for training large-scale 3D models with limited data; (iii) proposing novel generalist vision models that are able to tackle both 2D and 3D understanding; (iv) proposing novel techniques for training this cross-modality generalist vision model.
All Programs
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Visual Computing Center
Enzymatic Synthesis – Enzyme Characterization and Cascade Engineering
Academic Program: BioScience
Biocatalysis has found numerous applications in various fields as a green and sustainable alternative to chemical catalysis. The potential of using enzymes in organic synthesis is high, especially to make chiral compounds for pharmaceuticals. The project focuses on the expression, purification and characterization of various enzyme classes for different synthesis targets. The enzymes will be tested alone or in combination in aqueous catalysis reactions and the influence of composition on selectivity and reactivity will be studied. Project-duration will be 3-6 month, details of arrival/departure dates to be discussed.
BAS/1/1385-01-01
dominik.renn@kaust.edu.sa
Biocatalysis, Enzymes, Cascade Engineering, Enzyme Characterisation
Biochemistry
Students shall extend their general knowledge and skills in molecular biology and protein biochemistry. An emphasis will be put on expression, purification and characterization techniques. Students will be taught to work independently on projects, yet strengthening their critical sense to develop new ideas. In the course of the internship students shall demonstrate this understanding during oral presentations and one final written report.
BioScience
All Divisions
Graduate or Undergraduate
KAUST Catalysis Center
Biocatalytic Amine Synthesis
Academic Program: BioScience
Enantiomerically pure chiral amines are valuable building blocks for the synthesis of various compounds such as pharmaceutical drugs or agrochemicals. Biocatalytic amine synthesis allows a cost-effective and sustainable preparation of chiral amines in enantiomerically pure form. The project aims for the identification, engineering and application of new amine forming enzymes to biocatalytic retrosynthesis and new enzyme cascades. Project-duration will be 3-6 month, details of arrival/departure dates to be discussed.
BAS/1/1385-01-01
dominik.renn@kaust.edu.sa
Biocatalysis, Enzymes, Cascade Engineering, Amines
Biochemistry
Students shall extend their general knowledge and skills in molecular biology and protein biochemistry. An emphasis will be put on expression, purification and characterization techniques. Students will be taught to work independently on projects, yet strengthening their critical sense to develop new ideas. In the course of the internship students shall demonstrate this understanding during oral presentations and one final written report.
BioScience
All Divisions
Graduate or Undergraduate
KAUST Catalysis Center
The Development of Organic Transformations via Photocatalysis
Academic Program: BioScience
Academic Program: Chemistry
Over the past few years, the field of photocatalysis has demonstrated its potential to drive complicated chemical reactions under mild conditions using visible-light as an energy source and inexpensive, bench-stable substrates as feedstocks. Our group has been focused on the development of photocatalyzed organic transformations via diverse pathways including single electron transfer (SET), energy transfer (ET), photo-excited metal cross-coupling, electron-donor-acceptor (EDA) complex. Project-duration will be 3-6 month, details of arrival/departure dates to be discussed.
BAS/1/1385-01-01
chen.zhu@kaust.edu.sa
Photocatalysis
Chemistry
Students shall extend their general knowledge and skills in chemistry and photocatalysis. An emphasis will be put on characterisation techniques. Students will be taught to work independently on projects, yet strengthening their critical sense to develop new ideas. In the course of the internship students shall demonstrate this understanding during oral presentations and one final written report.
BioScience
All Divisions
Graduate or Undergraduate
KAUST Catalysis Center
Electro-catalyzed C-C and C-X bond cross-couplings
Academic Program: Chemistry
The development of sustainable and scalable catalytic methodologies to access structurally diverse organic compounds is a long-term aim for organic chemists. In this regard, organic electrocatalysis has been developed as an attractive catalytic platform making use of renewable electricity instead of stoichiometric oxidants or reductants. We are focusing on the construction of new C-C and C-X bonds and their mechanistic studies under electrochemical conditions with or without the aid of metal catalyst. Project-duration will be 3-6 month, details of arrival/departure dates to be discussed.
BAS/1/1385-01-01
chen.zhu@kaust.edu.sa
Electro-catalyzed, C-C bond formation, C-X bond cross-couplings, Electrocatalysis
Chemistry, Electrocatalysis
​Students shall extend their general knowledge and skills in chemistry, and electrocatalysis. An emphasis will be put on electrocatalysis steps and techniques. Students will be taught to work independently on projects, yet strengthening their critical sense to develop new ideas. In the course of the internship students shall demonstrate this understanding during oral presentations and one final written report.
Chemistry
Physical Sciences and Engineering
Undergraduate
KAUST Catalysis Center
Functional metagenomics: AI-based analysis of complex microbial interactions
Academic Program: BioEngineering
The amount of available protein sequence data is rapidly increasing, for example through applications of sequencing technologies to metagenomics. To understand biological phenomena on a molecular scale, it is crucial to determine the functions of proteins as well as their interactions. Experimental identification of protein functions will not scale to the current and rapidly increasing amount of available protein sequences. Function prediction methods using machine learning may be used to determine protein functions from their sequence. However, proteins rarely function alone but rely on other proteins to perform their function through direct and indirect interactions. The aim of the project is to computationally characterize the functions and interactions of proteins in metagenomes through the development and application of novel AI methods.
BAS/1/1659-01-01
robert.hoehndorf@kaust.edu.sa
metagenomics, Artificial Intelligence, machine learning, protein function prediction, microbiome, genomics
Bioinformatics
Month 1: identification of AI methods, characterization of metagenomics dataset, technical presentation Month 2: preparation and preprocessing of metagenomics data (QC, assembly) Month 3: implementation of AI method and data analysis, evaluation Month 4: combination of AI methods: protein functions and interactions between proteins Month 5: evaluation results, quantitative characterization Month 6: write-up
BioEngineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Computational Bioscience Research Center
Neuro-symbolic AI algorithms
Academic Program: Computer Science
Symbolic, logic-based languages are inherently interpretable by humans. Symbols are entities standing for other entities and can be combined to form more complex expressions. Symbol systems are therefore well suited to explain and answer questions of “how” and “why” an intelligent agent (human or artificial) arrived at a decision. Knowledge-based systems based on logic have traditionally been used successfully in question answering (formulated as computing entailments, i.e., statements that must be true if all the axioms are assumed to be true) and can generate novel and “surprising” answers through deductive inference. However, they are not well suited to dealing with incomplete or noisy information or identifying patterns from unstructured data. Machine learning methods, in particular neural networks, can deal with noisy and incomplete data substantially better than symbolic, logic-based methods. However, they operate mainly as black boxes which do not make the logic underlying a decision making process available. Neuro-symbolic methods in Artificial Intelligence aim to combine logic-based AI methods and neural methods to overcome the limitations of both. The aim of the project is the identify, implement, evaluate, and improve neuro-symbolic methods. The baselines and experiments will focus on one of two possible areas of application: biomedical data where a large number of knowledge bases has been developed, or common sense knowledge.
BAS/1/1659-01-01
robert.hoehndorf@kaust.edu.sa
neuro-symbolic, Artificial Intelligence, machine learning, Semantic Web, logic, bioinformatics, common sense reasoning, automated reasoning, knowledge graph, knowledge representation
Artificial Intelligence
Month 1: identification of algorithm, technical presentation Month 2: implementation, baseline experiments Month 3: algorithm evaluation Month 4: analysis, improvement and tuning Month 5: experimental results, theoretical results Month 6: write-up
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Computational Bioscience Research Center
Iproved oil recvery from carbonates
Academic Program: Energy Resources and Petroleum Engineering
Perform flow experiments on limestone cores. Propare and age cores. Understand principles of fluid flow in proous media and some of he involved equipment
BAS/1/1780-01-01
ksenia.kaprielova@kaust.edu.sa
petroleum, IOR, carbonate, microporous, EOR, chemical flooding
Petroleum or chemical engineering
Report with experimntl results
Energy Resources and Petroleum Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Nanovisualization
Academic Program: Computer Science

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

ASP/1/1669-01-01
ivan.viola@kaust.edu.sa
Nanovisualization
​computer science, applied mathematics, physics
​Students should work on an internship project and should implement a clearly specified task. It is expected that students are familiar with computer graphics and visualization and can document advanced skills in graphics programming​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Visual Computing Center
Formation of hydrogen peroxide in water microdroplet
Academic Program: Environmental Science and Engineering
Recent reports on the formation of hydrogen peroxide (H2O2) in water microdroplets produced via pneumatic spraying [1] or capillary condensation [2] have garnered significant attention. How covalent bonds in water could break under such mild conditions challenges our textbook understanding of physical chemistry and the water substance. While there is no definitive answer, it has been speculated that ultrahigh electric fields at the air-water interface are responsible for this chemical transformation. We are carrying out a comprehensive experimental investigation of H2O2 formation in (i) water microdroplets sprayed over a range of liquid flow-rates, the (shearing) air flow rates, and the air composition (ii) water microdroplets condensed on hydrophobic substrates formed via hot water or humidifier under controlled air composition. Our experimental results have challenged the putative claims of spontaneous H2O2 generation on the water surface [3, 4]. Additionally, new scientific questions along this theme have emerged that the VSRP intern will contribute to. References: 1. Lee, J. K.; Walker, K. L.; Han, H. S.; Kang, J.; Prinz, F. B.; Waymouth, R. M.; Nam, H. G.; Zare, R. N., Spontaneous generation of hydrogen peroxide from aqueous microdroplets. P Natl Acad Sci USA 2019, 116 (39), 19294-19298. 2. Lee, J. K.; Han, H. S.; Chaikasetsin, S.; Marron, D. P.; Waymouth, R. M.; Prinz, F. B.; Zare, R. N., Condensing water vapor to droplets generates hydrogen peroxide. P Natl Acad Sci USA 2020, 117 (49), 30934-30941. 3. Gallo Jr, A.; Musskopf, N. H.; Liu, X.; Yang, Z.; Petry, J.; Zhang, P.; Thoroddsen, S.; Im, H.; Mishra, H., On the formation of hydrogen peroxide in water microdroplets. Chemical Science 2022, 13 (9), 2574-2583. 4. Musskopf, N. H.; Gallo, A.; Zhang, P.; Petry, J.; Mishra, H., The Air–Water Interface of Water Microdroplets Formed by Ultrasonication or Condensation Does Not Produce H2O2. The Journal of Physical Chemistry Letters 2021, 12 (46), 11422-11429.
BAS/1/1070-01-01
peng.zhang@kaust.edu.sa
Physical Chemistry, Electrochemistry, Analytical Chemistry, Microfluidics, high-speed imaging, chemical kinetics
Physical Chemistry, Electrochemistry, Analytical Chemistry, Microfluidics, high-speed imaging, chemical kinetics
• The intern will conduct a comprehensive experimental investigation of H2O2 formation in (i) water microdroplets sprayed over a range of liquid flow-rates, the (shearing) air flow rates, and the air composition (ii) water microdroplets condensed on hydrophobic substrates formed via hot water or humidifier under controlled air composition. This will also entail a comparative assessment of the various H2O2 detection kits/assays available. • Glovebox experiments will be deployed to quantify H2O2 formation in water microdroplets as a function of the air-borne ozone (O3) concentration. • Effects of atmospherically relevant O3(g) concentrations (10–100 ppb) on the formation of H2O2(aq) will be evaluated. • Effects of the gas–liquid surface area, mixing, and contact duration will be quantified.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Water Desalination and Reuse Center
Growth and characterization of 2D materials using sputtering
Academic Program: Electrical Engineering
In this project, the student will be working on the growth of MoS2 using sputtering at different temperatures and characterizing them. The material will be later used in memory devices application.
BAS/1/1698-01-01
nazek.elatab@kaust.edu.sa
MoS2, sputtering, semiconductors, micro and nanofabrication
Electrical engineering, physics, solid-state devices, semiconductors, micro and nanofabrication
Growth of MoS2 using sputtering at different temperatures Characterization of the material - XRD, XPS, Raman, UV-Vis-NIR spectroscopy, AFM
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Dissecting the molecular basis of the neurodevelopmental features associated with Klinefelter syndrome
Academic Program: BioEngineering
Klinefelter syndrome (KS) is the most common chromosome aneuploidy in humans. Our laboratory recently established a unique cohort of KS-iPSCs carrying 47,XXY, 48,XXXY, and 49,XXXXY karyotypes. We apply a disease-modeling approach to investigate the molecular basis of the neurodevelopmental features associated with KS during differentiation of KS-iPSCs into neurons using the most advanced brain-organoids differentiation methods. The Laboratory of Stem Cells and Diseases is seeking an outstanding internship student to work on the study of the role of critical X-linked transcription factors. The selected candidates will combine human iPSC cultures and genome-editing (CRISPR-Cas9) techniques.
BAS/1/1077-01-01
Antonio.adamo@kaust.edu.sa
Disease-modeling, Brain-organoids, Stem Cells, iPSCs
Disease-modeling, Brain-organoids, Stem Cells
The candidate will successfully differentiate disease and healthy iPSCs into disease-relevant tissues applying the most advanced 3D brain-organoids differentiation techniques.
BioEngineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Integrated silicon photonics
Academic Program: Electrical Engineering
Integrated silicon photonics has sparked a significant ramp-up of investment in both academia and industry as a scalable, power-efficient, and eco-friendly solution. At the heart of this platform is the light source, which in itself, has been the focus of research and development extensively. We tackle this from two perspectives: device-level and system-wide points of view. In the former, the different routes taken in integrating on-chip lasers are explored from different material systems to the chosen integration methodologies. In the latter, we seek system-wide applications that show great prospects in incorporating photonic integrated circuits (PIC) with on-chip lasers and active devices, namely, optical communications and interconnects, optical phased array-based LiDAR, sensors for chemical and biological analysis, integrated quantum technologies, and finally, optical computing. By leveraging the myriad inherent attractive features of integrated silicon photonics, we aim to inspire further development in incorporating PICs with on-chip lasers in, but not limited to, these applications for substantial performance gains, green solutions, and mass-production. In this VSRP program, students will have the opportunity to learn the latest device design and fabrication of the integrated silicon photonics chips, and explore the applications based on their own interests. The project needs 3-6 months to be completed. Successful students can likely publish their results in prestigious scientific venues and get enrolled in the Ph.D program in Integrated Photonics Lab.
BAS/1/1700-01-01
alkhazraji_e@jic.edu.sa
integrated Si photonics
photonics Engineering, Physics, Mathematics
The students are expected to acquire the basic knowledge of the design of the integrated silicon photonics chips, proficient skills of device designs using simulation tools, hand-on experimental experiences of optoelectronic device characterizations, and conference/journal publications.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
3D CoraPrint and underwater superglue for coral restoration
Academic Program: BioEngineering
Due to environmental factors such as increasing heat and pollution, coral reefs are experiencing steady and substantial degradation over the years. 3D CoraPrint can produce porous coral scaffolds for coral nurseries with a sustainable material composed of calcium carbonate photopolymer material (CCP). Biocompatible ultrashort peptide hydrogels are used as scaffolding materials for 3D bioprinting. Our work shows that hydrogels can provide a favorable environment for cellular growth. We aim to combine our coral scaffolds with our field-approved environmentally friendly peptide-based superglue in order to glue coral microfragments to the 3D printed coral structures. Afterwards, the obtained structures will be printed/sprayed with peptide hydrogels to provide growth support. The growth and general health status of the treated glued fragments will be monitored over time both in nursery and in field.
BAS/1/1075-01-01
charlotte.hauser@kaust.edu.sa
3D orinting, corals, corals skeleton materials, coral reefs
tissue engineering, materials science, environmental science
In our vision, the proposed approach of combining in-house printing with CCP biocompatible and sustainable material and the use of an in-house designed biocompatible underwater superglue will allow the far-reaching goal of generating fast sustainable growing corals for coral reef restoration. Moreover the addition of hydrogel as a coral growth promoter will contribute to provide a better outcome. Key Milestones and Deliverables 1. Printing of porous CCP scaffold 2. Gluing of microfragments with superglue to the coral scaffolds 3. Printing/spraying of hydrogels as growth enhancers 4. Deployment in field and in nursery 5. Monitoring of coral growth/health
BioEngineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
3D bioprinting of biomimetic acute myeloid leukemia disease models
Academic Program: BioEngineering
Acute Myeloid Leukemia (AML) is a hematological malignancy of bone marrow (BM) origin characterized by the clonal expansion and differentiation arrest of myeloid progenitor cells. Worldwide the incidence of AML has been steadily increasing over the last three decades. AML remains a therapeutic challenge due to its high heterogeneity, with various subclones possessing distinct genetic and epigenetic alterations contributing to tumor functional differences such as drug resistance. We have developed a unique class of ultrashort self-assembling peptide hydrogels and proven in previous studies the potential use of these biomaterials as a 3D culture system for various cell types. This study aims to develop a 3D AML "organoid" model using advanced self-assembling peptides and patient-derived cellular components: primarily, establishing a model that closely recapitulates the tumor microenvironment and can be used in drug screening applications to enable personalized medicine therapeutics; ultimately, providing a platform that can help in answering unresolved questions regarding tumor development and niche organization.
BAS/1/1075-01-01
charlotte.hauser@kaust.edu.sa
leukemia, 3D cancer models, drug screening, 3D scaffolds
blood cancer, tissue engineering, nanomedicine, materials science
1. Identification and formulation of self-assembling peptide for the fabrication of 3D biomimicry models representative of the leukemic bone marrow niche i. Design and synthesis of suitable peptides and functionalization with bioactive moieties ii. Cytocompatibility testing of peptide hydrogel scaffolds iii. Establishment of 3D AML in vitro culture models 2. Investigation of leukemia microenvironment role on disease development and progression i. Investigation of exosomes role in tumor microenvironment regulation ii. Investigation of stromal cells role in tumor microenvironment regulation 3. Evaluation (Validation) of the 3D AML models as an in vitro model for drug screening i. Assessment of the effectiveness of the developed 3D biomimicry AML models in drug screening ii. Gene expression analysis and biomarkers identification
BioEngineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
3D model of colorectal cancer organoids
Academic Program: BioEngineering
Colorectal cancer (CRC) is the third most common malignancy worldwide, while it is the second most prevalent cancer for both males and females in Saudi Arabia. Intrinsic or acquired resistance to chemotherapy treatments - due to high inter- and intra-patient tumor genetic variability - causes 90% fatal disease relapse in patients already progressed to the metastatic stage. Therefore, the “one serves all” treatment principle is outdated and does not reflect our current knowledge of genetically-impacted patient-to-patient heterogeneity. Given the severity of the disease and its impact on society, there is an urgent need to develop a platform that will allow for advancing molecular and drug screening protocols for patients, in an effort to reach personalized treatment and to advise the medical staff. In this project, we aim to initiate the establishment of the first organoid platform for personalized genetic and drug screening in Saudi Arabia, based on 3D scaffolds made of own-developed ultrashort self-assembling peptides. We will use them in an organ-on-a-chip (OoC) system that will allow high-throughput screening of CRC constructs derived from patients’ samples under varying conditions. This system will allow the study of the genome and transcriptome for data analysis and modeling of the dynamics of the CRC microenvironment. We will develop a genetic database for risk variants within the Saudi population, and use the OoC to model the effects of drugs in CRC organoids. Once this platform is established, we will recreate in vitro the unique genetic background and tissue complexity of individual Saudi patients. All these technologies, the biomimetic peptides, the OoC system, the in silico modeling, and the genome/transcriptome analysis, will allow the further development of personalized medicine tools focusing on CRC molecular characteristics within the Saudi population. In summary, based on our experience in tissue engineering, biomaterial design, and in silico approaches, together with a strong team of medical doctors, and colorectal cancer biologists, we will investigate the capability of our self-assembling bioactive peptides for genetic and phenotypic 3D organoid cultures and their application in reliable personalized medicine, specifically when using OoCs as screening systems.
BAS/1/1075-01-01
charlotte.hauser@kaust.edu.sa
colorectal cancer models, 3D scaffolds, 3D bioprinting, ultrashort self-assembling peptides, tissue engineering
Tissue Engienerring, bioengineering, materials science
The general objective of this project is to establish colon organoid cultures starting from tissue material derived from the Saudi Arabian population cultured in 3D SAP smart scaffolds and to prove their use for personalized drug screening. Our first goal will be to generate a library of biofunctional SAPs and formulate an SAP-based hydrogel that accommodates the mechanical and biochemical properties needed for PDX-derived organoid development. We will evaluate the “organoid-formation capacity” using PDX-derived organoids and validate the transcriptome and exosome landscape of the PDX-organoids cultured within our novel biomaterial. At the same time, we will couple this material to a bioprinting system that will minimize the human manipulation of constructs. Next, we will use such technologies to fabricate PDOs from a Saudi cohort of patients with colorectal cancer. Finally, we will screen the genetic markers of CRC within the patient samples and assess appropriate chemotherapies within our system. The project is divided into Phases I-III with the following objectives described as individual tasks:
BioEngineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Integration of reservoir simulation with deep learning for subsurface reservoir management
Academic Program: Energy Resources and Petroleum Engineering
DSFT is a research team with diverse expertise including numerical modeling, machine/deep learning and energy system management. We are dedicating to technology development of advanced physics-driven numerical simulation and data-driven modeling for fluid flow in porous media. The goal of this visiting student project is to develop reservoir models to simulate the process of geological carbon storage, geothermal recovery or hydrogen storage, and ultimately use deep learning (e.g., convolutional/recurrent/graphic neural networks) or physics-informed neural networks to establish surrogate models for fast prediction of these nonlinear processes, and ultimately be ready for application of uncertainty quantification and also optimization. We seek for self-motivated, dedicated and creative students who wants to address challenging energy and environmental related engineering problems, whose majors are from petroleum engineering, computational mathematics, machine learning or closely related fields. Desired qualification will be competitive students with good skills of reservoir simulation, python or Julia programming and deep learning.
BAS/1/1423-01-01
bicheng.yan@kaust.edu.sa
Reservoir Simulation, pore-scale simulation, multiphase flow in porous media, deep learning
Petroleum Engineering, Computational Mathematics, Machine Learning
- report - prototype
Energy Resources and Petroleum Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Protein design based on AlphaFold2
Academic Program: Computer Science
AlphaFold2 has made the biggest breakthrough in computational biology and has created the hope to be able to not only solve the forward protein 3D structure prediction problem, but also target a more challenging but more practically useful inverse problem, protein design. Protein design is the core problem in protein engineering and optimization, with a very wide range of applications in enzyme optimization, antibody design, drug development, etc. This project is designed to leverage the power of AlphaFold2 to target the protein design problem through developing AI methods.
BAS/1/1624-01-01
xin.gao@kaust.edu.sa
Protein structure prediction, protein design, deep learning, AlphaFold
AI+Bioinformatics
An end-to-end learning pipeline for protein design.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Computational Bioscience Research Center
3D printing of smart composites for wireless structural health monitoring
Academic Program: Mechanical Engineering
New composite infrastructure used for the energy transition (hydrogen energy, solar, wind) are facing enormous challenges when it comes to integrity and maintenance. We propose here to integrate within smart composites special sensing technologies, allowing the wireless control of these critical systems. The project will make large use of additive manufacturing at different scales.
BAS/1/1315-01-01
gilles.lubineau@kaust.edu.sa
composites, sensors, wireless monitoring, damage in composites
Composites, additive manufacturing, energy, structural integrity
- report - prototype
Mechanical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Techno-economic uncertainty quantification and robust design optimization of hydrothermal and CO2-based geothermal systems
Academic Program: Earth Science and Engineering
The viability and sustainability of geothermal energy development are subject to several reservoir (subsurface) and economic parameter uncertainties. Determining the optimal operational parameters of the geothermal system, in the presence of uncertainty, require hundreds, if not thousands, of permutations of the different uncertain or naturally variable reservoir, operational and economic parameters for any specific hydrothermal system. The key task of this project is to quantify reservoir and economic uncertainties and to examine their effects on the techno-economic performance of the hydrothermal doublet system. What are the ranges of permissible reservoir conditions (parameters) to render a geothermal system economically viable? What are the trade-offs between these parameters? What are the ‘acceptable uncertainties’ and the ‘optimized values’ for a geothermal-energy producer in the economic decision-making? These kinds of questions will be addressed in this project, by means of scanning the related parameter-space via a very large number of numerical simulations, to then derive first-order ‘reduced model metrics’ that help to derive simplified decision-making quantities. In this study, we will develop a novel generalized non-dimensional expression of power generation over time to characterize the optimal operational parameters for various combinations of the reservoir parameters, and to determine which combination of the parameters depletes the reservoir heat the fastest. This methodology will further be applied to evaluate and compare the performance of geothermal systems that use in-situ brine or (sequestered) CO2 as the heat-extraction fluid, with emphasis on Saudi Arabia’s geological and economic conditions. Finite element/volume simulators such as TOUGH2/3, DARTS, or CMG, and wellbore-power system models will be used to conduct comprehensive simulations and to stochastically assess the system’s power and lifetime based on a range of available subsurface parameters (e.g., depth, thickness, permeability, porosity, permeability anisotropy), operational parameters (e.g., flow rate, injection temperature, well spacing) and economic parameters (e.g., drilling cost, heat cost, electricity cost, etc.). The candidate must have the willingness to learn basic geoscience and reservoir engineering concepts. Affinity with programming (preferably MATLAB/Python) and Paraview or related visualization tools is an advantage.
BAS/1/1339-01-01
chima.ezekiel@kaust.edu.sa
geothermal energy, techno-economic, uncertainty quantification, CO2 utilization and storage, optimization, energy systems, reservoir engineering
Earth Science and Engineering - Geothermal energy and Co2 utilization and geological storage
The intern will (i) carry out a literature study to compile a range of relevant subsurface inputs, (ii) build a parameterizable 3D reservoir model for the simulation of the geothermal system, (iii) develop an economic model for geothermal energy development in Saudi Arabia and, (iv) perform numerical simulations for optimization and uncertainty quantification of hydrothermal and CO2-based geothermal systems.
Earth Science and Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Seismic activity in the western part of the Arabian peninsula
Academic Program: Earth Science and Engineering
Earthquakes that can be felt in the western part of the Arabian peninsula occur along the Red Sea, the Gulf of Aqaba and several volcanic fields. Deep knowledge of the seismic activity is fundamental to understand geodynamic processes such as continental rift and seafloor spreading but also to assess the seismic hazard that threatens Saudi Arabia’s megaprojects such as NEOM and the Red Sea Development. In this project, you will contribute to advancing our understanding of the processes causing the current seismic activity in the study area. To do so, you will perform one or more of the following tasks: + seismic catalog analysis (Gutenberg–Richter distribution for estimation of b-value and magnitude of completeness); + estimation of Vp/Vs ratio from arrival-times of P and S waves; + estimation of minimum 1-D velocity model for earthquake locations; + applications of clustering algorithms to map seismically active faults; + development of a workflow for OBS data processing.
BAS/1/1339-01-01
laura.parisi@kaust.edu.sa
seismology, earthquakes, Red Sea
Seismology
A report with the main results of the research conducted during the internship
Earth Science and Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Digital Outcrop Model-based analysis of fracture network
Academic Program: Earth Science and Engineering
The presence of hydraulically conductive fracture networks is a key parameter influencing the fluid flow in hydrocarbon reservoirs, affecting the ultimate recovery factor, productivity and future development planning. This project aims to map and measure the fracture network in the Duqm area (Shuaiba Formation, Sultanate of Oman) using 3D and 2D photogrammetry data. We will analyze fractures at different scales to extract quantitative information about the spatial organization, the intensity and the variability with respect to the main faults observed in the area. For this project we are looking for a motivated geology student with a solid background in structural geology.
BAS/1/1399-01-01
yuri.panara@kaust.edu.sa
Geology, Structural Geology, Digital Photogrammetry, Fracture network
Structural geology
Define fracture set and fracture intensity variation of the area in relation with the main structural features.
Earth Science and Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Holocene sea level changes and geomorphological evolution Oman coast.
Academic Program: Earth Science and Engineering
The peninsula of Bar Al Hikman is an extremely flat area underwent a forced regression during the mid-Holocene resulting in the aerial exposure of a large (30km per 20 km) shallow lagoon. This project aims to document the spatio-temporal evolution of the coast and the processes involved in the deposition of carbonate sediments over the last 7000 years. We will analyse the geomorphologies of the actual and the ancient coastline (inland ; paleo-tidal channel, paleo-lagoon…) using satellite images and available drone models, as well as investigate remote sensing automatic workflows to extract quantitative information. For this ambitious project we are looking for a motivated student, geographer/geologist with a background in remote sensing and GIS.
URF/1/4097-01-01
thomas.teillet@kaust.edu.sa
Coast evolution / Sea level changes / satellite images / Holocene
Sedimentology, Coastal morphology evolution, Sea level changes,
Produce detailed geological maps (GIS) of the area highlighting geomorphologies and coastal evolution.
Earth Science and Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Microbial-related diagenesis in shallow marine carbonate sediments
Academic Program: Earth Science and Engineering
Micritization refer to process by which original carbonate grains are altered to cryptocrystalline textures (Bathurst, 1966). Although often recognised in ancient carbonate parasequences, there is little focus on the involvement of microbes in this process. The project aim at improving our understanding on where, when and at what rate microbially mitigated micritization can change original sediments. This intership aim to recognize microbial sediment alteration into the shallow marine sediment of 4 different locations (Red Sea, Arabian Gulf, Arabian Sea…) where we expect micritisation occurs. Core sediments will be analysed via various laboratory methods (granulometry, thin section, XRD and SEM). We are looking for an ambitious and motivated student with a geological and geo-microbiological background.
URF/1/4097-01-01
thomas.teillet@kaust.edu.sa
Carbonate, diagenesis, Microbes, sedimentology
Carbonate sedimentology, early diagenesis, geo-microbiology
Quantification and comparison of micritisation intensity in sediments of different locations (shallow marine lagoon environements)
Earth Science and Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Capturing adhesion molecules in action through imaging
Academic Program: BioScience
Cell adhesion occurs through spatio-temporally regulated interactions that are mediated by multiple intra- and inter-cellular components. Physiologically, shear forces on flowing cells orchestrate these interactions. The conventional assay used to study the effect of shear flow on cell adhesion is the parallel plate flow chamber (PPFC) assay, which records videos of cells rolling in flow and adhering to adhesion molecules on cells (eg. endothelial) or immobilized to a surface. However, due to the limited spatial resolution and sensitivity of these assays, nanoscopic molecular level mechanisms of selectin-selectin ligand interactions and their role in leukocyte (neutrophils, HSCs, T-cells) migration can’t be assessed. Recent developments in super resolution and single-molecule fluorescence imaging techniques allow for the visualization of individual molecules with nanometer spatial resolution and millisecond temporal resolution. Furthermore, advanced super-resolution microscopy provides unique opportunities to obtain information about nanometer-scale conformational dynamics of protein complexes as well as nanoscale architectures of biological samples. In collaboration with S. Habuchi (BESE, KAUST), whose research focuses on the development of tools and materials for fluorescence molecular imaging, we optimized the PPFC assay to image the cell using super resolution fluorescence microscopy. This allows us to image single molecular ligand architecture on the cell surface and determine how the ligand distribution was influenced as a result of rolling on selectin (E-/P-selectin) surfaces, both to each other (clustering) and to other selectin ligands. To characterize the dynamics of ligand distribution, we also developed real-time live cell imaging of these ligands under shear flow and are able to beautifully observe long, thin, flexible structures protruding out from the rear (tethers) and the front (slings) sides of the cell as it rolls over selectin expressing surfaces. For this project, these novel-imaging tools, combined with molecular, cellular and proteomic technologies will be used to further understand the cellular landscape that results before, during and following the migration of model cells such as hematopoietic stem cells.
BAS/1/1005-01-01
jasmeen.merzaban@kaust.edu.sa
Selectins, cell migration, metastasis, glycobiology
Cell Biology and Imaging
- overall goal is to develop a platform for imaging cells in flow over tissue samples expressing selectins - growth and maintenance of cell lines and primary cells - prepare and stain tissue sections for selectins (E-/P-/L-selectin) - prepare and stain cells lines for selectin ligands - run flow assays and use super resolution imaging under the supervision of an experienced PhD student
BioScience
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Machine learning for wireless communication systems
Academic Program: Electrical Engineering
At the CCSL, we are engaged in research and teaching on wireless communication methods for future wireless communication systems. In future wireless communication 5G and beyond, an extremely high number of heterogeneous devices, such as smartphones, sensors, robots, and vehicles, will communicate with each other. Consequently, the need for higher data rates and lower latency will increase significantly, posing major challenges for the resource allocation. Deep learning and data-driven algorithm approximation schemes have recently received significant attention as means to perform resource allocation with reduced complexity in 5G and beyond networks. This project considers the challenging case of Reflective Intelligent System (RIS) assisted, mmWave, frequency selective, massive MIMO systems with hybrid architecture and develops deep-learning based resource allocation frameworks. In these frameworks, prior data-set observations and deep neural network models will be leveraged to learn the mapping from received measurements to channels, beamformers and power allocations. Furthermore, deep neural networks will be used to approximate the optimization problems by selecting the suitable parameters that minimize the approximation error. The usage of a deep neural network framework reduces the computational complexity and processing overhead, since it only requires a limited number of layers of matrix-vector multiplications which can reduce processing time substantially.
BAS/1/1686-01-01
ahmed.eltawil@kaust.edu.sa
Machine Learning, MIMO, Wireless, ML
Electrical and Computer Engineering
Goal: This project aims to improve the overall performance on emerging and beyond 5G wireless systems in boosting system capacity with improved robustness and high data rates, via a low-cost, low-latency, and green implementation. For this purpose, deep learning or machine learning methods shall be applied, which can adapt to the dynamic changes of the wireless communication system and its environment and exploit the past experience to improve the future performance of the system. A report detailing system design and simulation results are expected at the end of the program.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Coral larvae settlement preferences on marine litter
Academic Program: Earth Science and Engineering
Coral larvae settlement is a crucial basis for reef sustenance. The introduction of artificial materials as waste into the marine environment potentially has significant effects on the settling behavior. Here we systematically test the influence of waste materials on the settling of coral larvae in an experimental setup.
BAS/1/1427-01-01
hildegard.westphal@kaust.edu.sa
Carbonate sedimentology, coral reef ecology
Reef ecology, carbonate sedimentology
The VSRP will be in charge of running the experiment, and for analysing the samples by optical imagery. The outcome will be summarized and discussed a comprehensive report.
Earth Science and Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Red Sea Research Center
Micro-devices for AR and VR head-mounted displays
Academic Program: Electrical Engineering
Micro-LED displays or laser displays will be useful for head-mounted displays for virtual reality (VR) and augmented reality (AR). This project aims to develop micro-LEDs and vertical-cavity surface-emitting lasers (VCSELs). Those devices are made from nitride semiconductors grown by the original MOVPE method at KAUST. Also, study material science of nitride semiconductors and their quantum structures.
BAS/1/1676-01-01
kazuhiro.ohkawa@kaust.edu.sa
Optoelectronics, Semiconductor, LED, LD, VCSEL, laser, micro-LED, Physics, Electronics
Optoelectronics
Realizing novel micro-LEDs and VCSELs in RGB, especially the red region is the most difficult topic for worldwide scientists and researchers.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate or Undergraduate
Identifying novel Cas variants for pathogen diagnostics
Academic Program: BioEngineering
Rapid, point-of-care (POC) diagnostics are essential to mitigate the impacts of current (and future) epidemics; however, current methods for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) require complicated laboratory tests that are generally conducted off-site and require substantial time. CRISPR-Cas systems have been harnessed to develop sensitive and specific platforms for nucleic acid detection. These detection platforms take advantage of CRISPR enzymes’ RNA-guided specificity for RNA and DNA targets and collateral trans activities on single-stranded RNA and DNA reporters. Microbial genomes possess an extensive range of CRISPR enzymes with different specificities and levels of collateral activity; identifying new enzymes may improve CRISPR-based diagnostics. We work to identify new Cas variants, and characterize its catalytic activity.
BAS/1/1035-01-01
norhan.hassn@kaust.edu.sa
Bioengineering, diagnostics, CRSIPR
Bioengineering
Training on different molecular biology techniques and comprehending the components and mechanisms of the CRSIPR-Cas systems.
BioEngineering
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Investigating the carrier dynamics of emerging wide band gap semiconductor for novel optoelectronic applications
Academic Program: Marine Science
Academic Program: Materials Science & Engineering
Studying fundamental sciences is the key factor for technological development, as this allows the researchers to better understand the natural phenomena behind human discoveries. In the context of semiconductor innovation, for example, the physics principles of carrier recombination and the importance of quantum mechanics, including carrier quantum confinement, localization, and their effects on the carrier wavefunction, need to be studied in order to ensure efficient semiconductor-based devices. Mainly, technology based on wide bandgap semiconductors as high-energy optoelectronics based on these materials that operate at the deep UV and UV spectral ranges got scientists attention due to their use for many applications in different fields, such as medical treatment, astronomy investigations, material analysis, missile detection, space communications, security systems, and x-ray imaging. Due to the lack of suitable substrates, the emitting devices still need further enhancement. For example, no commercial laser diode operates in the UV spectral range below 375 eV. Therefore, in this project, this issue will be addressed to enhance the emission of these materials. The project will focus on studying the fundamental physics of the carrier dynamics of wide-bandgap semiconductors such as III-nitrides and oxides. Several structures will be investigated experimentally and theoretically for developing wide bandgap semiconductor-based materials, one of which is carrier confinement using different novel approaches. The project goals will be achieved by employing time-integrated photoluminescence (PL) using CW lasers as well as time-resolved PL using ultrafast oscillators attached to streak camera or photon-counting detection systems. In addition, PL excitation will be used to understand the origin of the emitted light using a Xe lamp attached to a fluorescence system. The theoretical simulation of emitting devices will be carried out by analytical code, such as Lumerical.
BAS/1/1319-01-01
iman.roqan@kaust.edu.sa
Wide bandgap semiconductor, LED, laser diode, emitting devices, DUV
Optical spectroscopy of semiconductors
- Be familiar with advanced optical spectroscopy techniques - Understanding the fundamentals of optical properties of semiconductor - Understanding the carrier dynamics of semiconductor - Achieving theoretical and experimental results of the novel semiconductor structure - Analyzing the carrier dynamic outputs - Finally, concluding the desired projects
Marine Science
Physical Sciences and Engineering
Graduate or Undergraduate
Organic Solvent Nanofiltration
Academic Program: Chemical Engineering
Separation processes play a remarkable role in the chemical and pharmaceutical industries, where they account for 50 to 70% of both capital and operational costs. Organic synthesis in the chemical and pharmaceutical industry are frequently performed in organic solvents and consist of products with high added value that should be removed from the organic solvents. Organic solvent nanofiltration is an emerging technology which allows the isolation and separation of solutes between 50 and 2000 g/mol in organic solvents. The development of nanofiltration membranes stable in harsh environments (e.g. polar aprotic solvents, extreme temperature, pressure and pH) is of utmost importance. Solvent resistant nanofiltration membranes will be fabricated exhibiting superior chemical stability compared to commercial polymeric membranes. The concept of design of experiments (DoE) will be applied throughout the project to gain an in depth understanding parameters governing the membrane will be prepared and crosslinked using aromatic bifunctional crosslinkers. The membrane performance (i.e. flux, retention profile, solvent stability) will be evaluated using a nanofiltration rig.
BAS/1/1401-01-01
gyorgy.szekely@kaust.edu.sa
Organic solvent nanofiltration, membrane fabrication, membrane separations, hybrid processes
Engineering in Pharmaceutical Industry
The student will acquire soft skills such as team working, project and time management, giving oral presentations. By the end of the traineeship the student will have a deep understanding of membrane separations, particularly in nanofiltration. Practical and theoretical aspects of surface modification techniques and polymer chemistry techniques will be acquired.
Chemical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Safeguarding our daily bread from wheat rust diseases
Academic Program: Plant Science
Wheat rusts are destructive diseases of wheat, which throughout recorded history have caused devastating epidemics almost wherever wheat is grown. The wild ancestors of domesticated wheat represent a rich source of genetic variation with huge potential for improving disease resistance. Deploying this genetic diversity into elite, cultivated wheat by traditional breeding takes many years for just a single resistance gene. However, the molecular identification (cloning) of resistance genes opens up new possibilities for accelerated breeding by marker-assisted selection and genetic engineering [Refs. 1,2]. The Wulff lab has established a suite of molecular plant breeding technologies that significantly reduce costs and accelerate plant growth [Refs. 3,4], gene discovery [Refs. 5,6,7,8,9] and gene cloning [Refs. 7,8,9,10]. You will use our tools, structured germplasm, and sequence resources to characterize novel candidate rust resistance genes. You will be supervised by Brande Wulff and receive training and co-supervision from a team of Postdocs and PhD students with expertise in bioinformatics, mathematics, scripting, genetics, plant pathogen interactions, wheat husbandry and crossing. KAUST is a vibrant place to discuss and plan science. You will become part of the larger KAUST community and alumni, which we hope will have lasting positive impacts on your future career. References [1] Dhugga & Wulff (2018). Science 361:451-452. [2] Luo et al (2021) Nature Biotechnology 39:561-566. [3] Watson et al (2017) Nature Plants 4:23-29. [4] Ghosh et al (2018) Nature Protocols 13:2944-2963. [5] Steuernagel et al (2015) Bioinformatics 31:1665-7. [6] Steuernagel et al (2020) Plant Physiology 183:468-482. [7] Arora et al (2018) Nature Biotechnology 2:139-143. [8] Gaurav et al. (2020) bioRxiv doi.org/10.1101/2021.01.31.428788. [9] Steuernagel et al (2016). Nature Biotechnology 34:652-5. [10] Sánchez-Martín et al (2016). Genome Biology 17:221.
BAS/1/1091‐01‐01
brande.wulff@kaust.edu.sa
Wheat, wheat rust, resistance genes, GWAS, bioinformatics, plant disease, cloning, food security
Plant genetics
Research experience including learning of one or more techniques employed in the lab, the generation of original data, design of figure(s), and presentation of results at lab meeting.
Plant Science
Biological and Environmental Sciences and Engineering
Graduate or Undergraduate
Center for Desert Agriculture
Design and 3D print a continuous flow reactor
Academic Program: Chemical Engineering
High-added value chemicals and active pharmaceutical ingredients (APIs) are normally produced using batch technology. Some of those chemical compounds can be produced in continuous flow reactors (i.e. continuous manufacturing). Advanced techniques of manufacturing like 3D printing allow us to produce specific reactors that are optimized to produce a certain chemical or pharmaceutical component. Such novel designs are of importance when heat of reaction needs to be evacuated faster or when improved mixing is required, etc. The current project aims to use digital tools to produce custom designs of continuous flow reactors. The student will also be involved in the production of the reactor and on its flow pattern characterization (determination of residence time distribution).
BAS/1/1420-01-01
carlos.grande@kaust.edu.sa
reaction: 3D printing: simulation: digitalization
reaction engineering ; transport phenomena ; digitalization
Learn how to produce novel designs and transform them into high quality meshes for 3D printing. Rhino3D and Grasshopper will be used for parametric geometry design. Categorize the advantages, but also the limitations of 3D printing as a manufacturing tool for chemical reactors. Understand residence time distribution concepts and participate in a joint scientific publication.
Chemical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Advanced Membranes and Porous Materials Center
Modelling tools for advanced separation processes
Academic Program: Chemical Engineering
Adsorption processes are emerging separation technologies that have the potential to cut energy consumption in many different separations vital to society. Differently from other separation technologies, adsorption processes are transient processes where the operation of a bundle of columns is synchronized to operate in an efficient manner. Modelling the performance of adsorption processes involve the utilization of advanced modelling tools that will be learnt in this project. The student will learn how to develop a mathematical model for a given gas separation and to use gPROMS language as a tool to numerically solve this problem. The main objective of this project is to learn how to model adsorption processes in general so there is plenty of flexibility in selecting the separation to be targeted.
BAS/1/1420-01-01
carlos.grande@kaust.edu.sa
adsorption: separation: 3D printing: simulation: digitalization
separation processes ; transport phenomena ; modelling
Learn to use gPROMS software and how to transform equation-based problems into computer code. Understand the important variables that are important for simulation and optimization of adsorption processes. Participate in a joint scientific publication.
Chemical Engineering
Physical Sciences and Engineering
Graduate or Undergraduate
Advanced Membranes and Porous Materials Center
Modeling Fluid Flow and Transport in Porous Media by Physics-driven Simulation Approaches
Academic Program: Energy Resources and Petroleum Engineering
The Digital Subsurface Flow & Transport Lab (DSFT-Lab) is a cross-discipline team led by Prof. Bicheng Yan KAUST. Currently DSFT focuses on physics-driven and data-driven (deep learning mainly) model development that can simulate multiphase flow in porous media at reservoir scale and pore scale. The goal is to explore the fundamental physics that governs the complex and coupled physics related flow and transport in porous media, and investigate its impact on subsurface fluid flow such as hydrocarbon recovery, geologic carbon sequestration, hydrogen storage and geothermal recovery etc. DSFT seeks for self-motivated, dedicated and creative STEM-majored students who wants to address challenging energy and environmental related engineering problems.
BAS/1/1423-01-01
bicheng.yan@kaust.edu.sa
Reservoir Simulation, pore-scale simulation, multiphase flow in porous media, deep learning
Subsurface Modeling
* Subsurface modeling workflow/software for reservoir or pore scale modeling; * 1 to 2 publications based on the scientific findings.
Energy Resources and Petroleum Engineering
Physical Sciences and Engineering
Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Fully 3D Printed Flexible ECG Patch with Dry Electrodes
Academic Program: Electrical Engineering
The project aims to design and develop a fully 3D Printed Flexible ECG Patch with Dry Electrodes. The student will work on the design and 3D printing of different conductive materials such as graphene and carbon nanotubes, and testing them as ECG dry electrodes. The student will be characterizing the reliability of the electrodes using a semiconductor device analyzer ad probe station, in addition to the endurance of the electrodes after performing several bending, twisting and stretching cycles.
BAS/1/1698-01-01
nazek.elatab@kaust.edu.sa
3D printing, ECG, flexible electronics
3D Printed Electronics
1- Design and 3D printing of ECG electrodes and substrate based on different materials combinations 2- Electrical (skin impedance) and mechanical testing of the electrodes (bending, stretching, and twisting cycling tests) 3- Testing of the comfort to the wearer
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Graduate
Efficient pricing of high-dimensional (multi-assets) European Options
Academic Program: Applied Mathematics and Computer Science
The student will work on designing new numerical methods based on hierarchical adaptive sparse grids quadratures combined with Fourier techniques for efficient pricing of high-dimensional (multi-assets) European Options. Specifically, the student will contemplate the possibility of finding a heuristic framework for an optimal choice of the integration contour (damping parameter) which controls the analyticity of the integrand in the Fourier space and hence accelerate the performance of the quadrature methods. He will also develop a systematic comparison between hierarchical deterministic quadrature methods, Tensor Product (TP) quadrature, Smolyak (SM) Sparse Grids quadrature, and Adaptive Sparse Grids (ASG) quadrature to numerically evaluate the option price under different pricing dynamics, Geometric Brownian Motion (GBM), Variance Gamma (VG) and Normal Inverse Gaussian (NIG) for different multi-asset payoff functions such as Basket Call/Put and Rainbow options. The student is also asked to elaborate a comparison in terms of computational complexity against the quadrature methods for different dimensions, and various combination of parameter sets within the mentioned pricing models.
400000024
chiheb.benhammouda@kaust.edu.sa
Multi-Asset Option Pricing, Fourier Transform, Lewis Valuation Approach, Damping Parameters, Lévy models, Hierarchical Adaptive Sparse Grids Quadrature, Monte Carlo
Computational Finance, Computational Mathematics, Numerical Analysis
As the main project deliverable, we expect a scientific report (eventually a research manuscript) including a detailed description and analysis of the proposed methodology developed within the course of the internship and providing all numerical experiments to showcase the versatility of the proposed heuristic framework. The working environment the student will use should include a GIT repository shared with the project collaborators in which he includes all project-related materials such as progress reports, codes, figures, and important references from the literature to facilitate the supervision task and communicate ideas more effectively.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Learning to model geophysical processes with NNs
Academic Program: Earth Science and Engineering
Scientific machine learning (SML) is an emerging field focused on leveraging machine learning across different sciences whose natural processes are governed by well-established physical equations. Early successes have been reported by the geophysical community in terms of modelling various wave phenomena by means of PINNS, e.g. eikonal equation (bin Waheed et al., 2021) and wave equation (Alkhalifah et al., 2021). Alternative solutions have recently emerged in the SML community with the aim of overcoming some of the limitations of PINNs and extending applicability from functionals to operators. This project will investigate such approaches in the context of geophysical modelling and inversion and identify the benefits and limitations when compared to traditional modelling methods as well as PINNs. References: - U. bin Waheed, E. Haghighat, T. Alkhalifah, C Song, Q Hao, 2021, PINNeik: Eikonal solution using physics-informed neural networks, Computers & Geosciences. - T. Alkhalifah, C. Song, U. Waheed, Q. Hao, 2021, Wavefield solutions from machine learned functions, arXiv preprint arXiv:2106.01433.
BAS/1/1414-01-01
matteo.ravasi@kaust.edu.sa
geophysics; modelling; machine learning;
Geophysics
The candidate will be tasked with: - Literature review of ML methods for learning physical phenomena governed by PDEs. - Develop and implement one of the methods for a geophysical PDE of choice. - Compare pros and cons of the developed method against state-of-the art numerical modelling methods and PINNs.
Earth Science and Engineering
Physical Sciences and Engineering
Undergraduate
Resilient Models for Attacks Detection in Cyber-Physical Systems
Academic Program: Electrical Engineering
The goal of this project is developing methods to merge Machine Learning (ML) with physics-based models to create control algorithms can significantly enhance the resiliency of Cyber-Physical Systems (CPS). The approach will combine recent results in ML with control theory via constrained optimization to create novel systems and methods for protecting CPS from malicious cyber intruders via detection and prevention strategies. Specifically, the research project focuses on (1) refining the offline/online training and execution algorithms of ML models through physics-based constrained optimization, (2) developing secure estimation and control algorithms that are significantly more resilient to cyber-attacks than the state-of-the-art counterparts, and (3) improving the distributed resiliency for networked systems supporting multi-agent autonomous systems.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
Cyber-physical systems, resiliency, cybersecurity, control methods, machine learning, physics-based methods.
Electrical and Computer Engineering/Computer Science
The goal of this internship is to analyze and improve existing data-driven and physics-based algorithms for attack detection in CPS. The student will be expected to learn about existing solutions, as well as the challenges and requirements to applying such techniques in their settings. With guidance of other team members, the student will then find new solutions for improving algorithmic resiliency in order to reduce cyber-risks related to the CPS operation. Candidates should be motivated to work on research-oriented problems with a team and develop new solutions. They should have a strong background in controls, in particular with regards to machine learning and power systems. They are also expected to be proficient in MATLAB/Simulink.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Real-Time (co-)Simulation for Cybersecurity
Academic Program: Computer Science
The objective of this project is to use Real-Time Simulation (RTS) and co-simulation models for real-time prediction and control of disturbances related with attacks propagation. Essentially, the goal is to study how RTS via real-time communication interfaces of different RTS tools can run systems security, resilient and isolation algorithms in order to monitor and control the system under study.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
Real-time simulation, cybersecurity, attacks, detection, prevention.
Electrical and Computer Engineering/Computer Science
The goal of this project is to develop and implement models in RTS using OPAL-RT hardware and software to study how cyber-attacks effects can propagate in real-time and investigate controls able to isolate such impact. Students should have backgrounds and experience using real-time simulators software such as RT-LAB, HYPERSIM, RSCAD, etc.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Regaining Trust in IoT
Academic Program: Computer Science
While embedded devices play an increasingly significant role in the functional interoperability and coordination of various IoT systems, they are often developed without security in mind. The premise of this research is that effective security solutions can benefit from assistance by a trustworthy hardware root of trust. This research involves approaches that utilize device intrinsic (non-)silicon-based physical/hardware characteristics to verify, authenticate, and trust IoT devices. The goal is for such methods is to support and improve detection of security breaches in the device, network, or/and system level within critical information infrastructures. This can be achieved by leveraging the acquired hardware-level values in order to verify the integrity of each layer in a single device as well the integrity of the process variables within a control operation.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
IoT, security, embedded systems, hardware security.
Computer Science and Engineering
1. Experimental evaluation of an existing hardware-based security approach. 2. Design and implementation of an improved approach incorporating a mixture of hardware signals: build an intrusion detection model capturing any malicious activity in any layer of the system stack.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Cyber-Secure Integration of Renewable Energy Sources
Academic Program: Electrical Engineering
This research project aims to contribute towards a resilient and secure power system employing power electronics for renewable energy sources (RES) integration. The goal is to examine the security implications of integrity and availability attacks in power transmission and distribution systems, and examine the performance and economic impact on real-time energy market operations. Risk assessment methodologies will quantify attack resources under certain level of adversarial knowledge. The objective is to enhance system cyber-(resiliency, security), while addressing disruptive cyber-physical events and preventing these effects from escalating into major failures and hence causing cascading blackout scenarios.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
Renewable energy, cybersecurity, power electronics, prevention, cyber-physical energy systems.
Electrical and Computer Engineering/Computer Science
1. Improve an existing power system benchmark to support certain RES and their corresponding controllers. 2. Impact evaluation of attacks at the various connection points. 3. Examine effects of attacks on market operations. 4. Identify methods to improve system performance.
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Ransomware in Industrial Control Systems
Academic Program: Computer Science
According to a recent report, ransomware attacks on industrial entities increased more than 500% from 2018 to 2020. What is more, in 2020, ransomware, targeted ransomware, supply chain breaches and cloud connectivity all emerged as top-of-mind concerns for security teams at industrial enterprises. One of the biggest cyberattacks in history – the SolarWinds Orion supply chain breach – impacted as many as 18,000 organizations, many of which were industrial enterprises with physical operations. As a result, this project will study effective defense-in-depth security strategies, ensure an understanding of network interdependencies, and conduct crown jewel analysis to identify potential weaknesses that could disrupt business continuity and production in the event of ransomwares.
BAS/1/1692-01-01
charalambos.konstantinou@kaust.edu.sa
Ransomware, industrial control systems, cybersecurity, critical infrastructure.
Computer Science and Engineering
In this project, the students will examine the security issues of supply chain ransomware in industrial control systems (ICS) environments. One direction would be to investigate the applicability of unidirectional gateway technology to provide robust protection from such targeted attacks. Other methods include investigation of deep learning malware detector indicators.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Machine learning techniques for divergence-free field reconstruction
Academic Program: Applied Mathematics and Computer Science

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

BAS/1/1604-01-01
raul.tempone@kaust.edu.sa
Machine Learning, Deep Neural Networks, Dynamical Systems, Numerical Analysis, Stochastic Numerics
Machine learning
As the main project deliverable, we expect a scientific report describing the methodology developed in the internship and its numerical use in various applications. The working environment the student will use should include a GIT repository for all project-related materials to facilitate proper verification processes. These materials include, among others, the codes and the saved input-outputs corresponding to all tested cases.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate
Concentrated Solar Thermal Energy for Mineral Processing
Academic Program: Mechanical Engineering
This research project will assess the efficacy of using concentrated solar thermal energy in processing mineral ores. Students will have the choice to work on: 1. Low carbon calcination of Limestone and Clay for Cement production 2. Use of solar energy for Ore shock pretreatment to reduce comminution energy 3. Renewable steam for ores calcination through H2/O2 combustion Most projects will involve both experimental and computational research.
BAS/1/1411-01-01
bassam.dally@kaust.edu.sa
Solar, Renewable, Energy, Combustion, Mechanical, Engineering
Renewable Energy
1. Parametric study to quantify effect of solar concentration, temperature, type of ore, size of particles etc.. 2. System level analysis to assess the different aspects that make it techno-economically viable 3. Design, build and test hybrid combustion of renewable fuels and solar thermal for mineral processing
Mechanical Engineering
Physical Sciences and Engineering
Graduate
Clean Combustion Research Center
Machine Learning and Dynamical Systems
Academic Program: Applied Mathematics and Computer Science
The student will work on machine learning techniques applied to the study of dynamical systems. Specifically, the student will use different Neural Network architectures to approximate the equations governing a given system's evolution. This evolution may be intrinsically random, or we may add randomness to contemplate the possibility of approximating a large dimensional system by a low dimensional one. He will also test this methodology in several applications and report his results to get familiar with the problem and its input data.
4000000024
erik.vonschwerin@kaust.edu.sa
Machine Learning, Deep Neural Networks, Dynamical Systems, Numerical Analysis, Stochastic Numerics
Computational and Applied Mathematics
As the main project deliverable, we expect a scientific report describing the methodology developed in the internship and its numerical use in various applications. The working environment the student will use should include a GIT repository for all project-related materials to facilitate proper verification and feedback processes. These materials include, among others, the codes and the saved input-outputs corresponding to all tested cases
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Developing bioinformatic tools for Multi-omic data integration
Academic Program: BioScience
The past two decades have provided unprecedented growth in data modalities and data availability within the biomedical research. As a result, methodologies for the multi-omic analysis of bulk (average profiles derived from hundreds to millions of cells), single-cell data or both are in continuous development. Our team has been involved in such developments working on multi-omic frameworks (STATegra framework + STATEgRa Bioconductor package), multi-omic DeepLearning based analysis (LIBRA - BioarXiv) and specific tools such as GeneSetCluster to summarize information derived from gene-set enrichment analysis. We are looking for highly motivated and skilled visiting students to work on one or several of the following challenges sub-projects: - Upgrading STATegRA Bioconductor package to account for the novel developments, including the single-cell analysis applications and the integration with GeneSetCluster analysis tool. - Novel Deep Learning-based methodologies for multi-omic analysis. - Implementing Cell-to-cell interaction analysis as part of a multi-omic integrative framework. We have existing proprietary data to work in the context of the Bone Marrow. For any selected student, the project to be conducted will be decided based on the student's interest, technical proficiency, and level of study. We expect for any participant (a) to bring motivation, enthusiasm, creativity, and hard work, (b) give lab seminars on your work, (c) collaborate with other lab members, and (d) produce a final written report. References of interest: - STATegra framework: https://www.frontiersin.org/articles/10.3389/fgene.2021.620453/abstract - STATegRa package: https://www.bioconductor.org/packages/release/bioc/html/STATegRa.html - LIBRA single-cell multi-omic framework: https://www.biorxiv.org/content/10.1101/2021.01.27.428400v1 Link to recent publications of the team: http://www.lunacab.org/publications/
BAS/1/1093-01-01
David.gomezcabrero@kaust.edu.sa
Single-cell analysis, multi-omic analysis, Deep Learning,
Biosciences, bioinformatics
Enhancing critical thinking, presentations skills, and scientific writing. The research, in collaboration and with support of team members, may lead to scientific publications. Obtaining a hands-on perspective at the frontier of bioinformatics, multi-omic analysis, and its applications in an interdisciplinary research group and environment.
BioScience
Biological and Environmental Sciences and Engineering
Undergraduate
Bringing Chromatin Conformation and Spatial profiling into clinical research
Academic Program: BioScience
The past two decades have provided unprecedented growth in data modalities and data availability within the biomedical research. As a result, methodologies for the multi-omic analysis of bulk (average profiles derived from hundreds to millions of cells), single-cell data or both are in continuous development. Our team has been involved in such developments working on multi-omic frameworks (STATegra framework + STATEgRa Bioconductor package), multi-omic DeepLearning based analysis (LIBRA - BioarXiv) and specific tools such as GeneSetCluster. As a next step, we aim to integrate into current integrative pipelines additional data-types such as Chromatin Conformation and Spatial profiling. We are looking for highly motivated and skilled visiting students to work on one or several of the following challenges sub-projects: - Integrating Chromatin Conformation information (such as HiC) in clinical oriented research. Myeloma as a case study. Proprietary data is available for such integration. - Integrating Spatial Transcriptomics and Cell-to-cell interaction analysis as part of a multi-omic integrative framework. We have existing proprietary data to work in the context of the Bone Marrow. For any selected student, the project to be conducted will be decided based on the student's interest, technical proficiency, and level of study. We expect for any participant (a) to bring motivation, enthusiasm, creativity, and hard work, (b) give lab seminars on your work, (c) collaborate with other lab members, and (d) produce a final written report. The project relies on multi-skilled collaborations involving biologists, clinicians, bioinformaticians and computational biologists. References of interest: - STATegra framework: https://www.frontiersin.org/articles/10.3389/fgene.2021.620453/abstract - STATegRa package: https://www.bioconductor.org/packages/release/bioc/html/STATegRa.html - LIBRA single-cell multi-omic framework: https://www.biorxiv.org/content/10.1101/2021.01.27.428400v1 - 3D data: https://www.nature.com/articles/s41467-020-20849-y
BAS/1/1093-01-01
David.gomezcabrero@kaust.edu.sa
Single-cell analysis, Multi-omic analysis, Deep Learning, Chromatin Conformation, Spatial Transcriptomics, Translational research
Biomedicine
Enhancing critical thinking, presentations skills, and scientific writing. The research, in collaboration and with support of team members, may lead to scientific publications. Obtaining a hands-on perspective at the frontier of bioinformatics, multi-omic analysis, and its applications in an interdisciplinary research group and environment.
BioScience
Biological and Environmental Sciences and Engineering
Undergraduate
Numerical approximation of partial differential equations
Academic Program: Applied Mathematics and Computer Science
The numerical approximation of partial differential equations is a relevant field of applied mathematics. Several projects are available at my research group, ranging from purely theoretical studied to more applied and computational tasks. Theoretical project will involve the study and analysis of the properties of the approximation of some partial differential equations. Computational projects are related to the implementation and testing of research codes for scientific computing. The areas of applications include electromagnetism, structural mechanics, fluid-dynamics, fluid-structure interactions.
BAS/1/1688-01-01
daniele.boffi@kasut.edu.sa
Numerical Analysis, Applied Mathematics, Scientific Computing
Numerical analysis, Applied Mathematics, Scientific Computing
Implementation and testing of the proposed algorithms. Study and analysis of the properties of approximating schemes for partial differential equations.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Causal and Fair Machine Learning
Academic Program: Computer Science
The exact topic depends on the student's interest, student's background, and previous research experience. Generally speaking, there are mainly three topics of this project: 1. Designing Fair Machine Learning Algorithms. In this project, students will focus on how to make the current machine learning algorithms be fair. They will also explore the fairness issue of the current machine learning algorithms, especially for healthcare data. 2. Causality as a tool for de-biasing current deep learning algorithms. Students will use the idea of causality to different deep learning tasks to de-bias the datasets or algorithms in order to improve the accuracy and trustworthiness. 3. Causality as a tool for invariant learning. This project mainly focuses on transfer learning, students will use causality to design transfer learning algorithms.
BAS/1/1689-01-01
di.wang@kaust.edu.sa
Machine Learning, Deep Learning
Causal Inference, Fairness, Transfer Learning, Deep Learning
During the project, students will have opportunity to learn about some topics in trustworthy machine learning, especially fair learning, transfer learning and causal learning. They will learn and implement the SOTA methods. Hopefully, they may produce some publication after the intern.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Robust/Differentially Private Machine Learning
Academic Program: Applied Mathematics and Computer Science
The topic is flexible and depends on student's background, mathematical knowledge, previous research experience. Generally, this project mainly focuses on how to design robust (especially robust against to outliers or heavy-tailed distributions) or private (or forgettable) algorithms for some foundamental problems in machine learning, deep learning or statistics. Students will provide theoretical guarantees via using mathematical tools from probability, learning theory, optimization and high dimensional statistics. Also, student will analyze utility-privacy tradeoff or robustness-utility tradeoff.
BAS/1/1689-01-01
di.wang@kaust.edu.sa
Machine Learning, Privacy, Statistics
Machine Learning, Data Privacy, High Dimensional Statistics
Students will learn some fundamental techniques and results in learning theory, high dimensional statistics, optimization and differential privacy. They will also implement machine learning or statistics algorithms via using Matlab or Python. Hopefully they could have publications after the project.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Foundations of Private and Fair Statistics
Academic Program: Statistics
The topic is flexible and depends on student's background, previous experience and mathematical skill. Specifically, in the topic, students will explore basic statistical model or problems for different types of data in the differential privacy or fairness model. For statistical model , topics include component analysis, supervised learning, mixture model , et al. For data, topics include survival data, functional data, network analysis. Students will design new algorithms to solve these problems and provide theoretical guarantees on the utility-privacy or utility-fairness tradeoff. Moreover, they will implement these algorithms on some sensitive data such as healthcare and biomedical data.
BAS/1/1689-01-01
di.wang@kaust.edu.sa
Machine Learning, Statistics, Privacy
Machine Learning, Data Privacy, Statistics
Students will learn basic techniques and terminologies in differential privacy, fairness and some topics in statistics. They will have the chance to implement the best-known algorithm for some specific problems. Hopefully, they could have some new results and publications after the project.
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Undergraduate
Protocol development for biomass quantification in membrane autopsies
Academic Program: Environmental Science and Engineering
A membrane autopsy is a valuable tool used in membrane (bio)fouling studies. One of the objectives of the autopsy is to quantify the amount of biomass. The current method involves sonication of the sample, followed by filtration and DOC measurement. The objective of the project is to investigate the influence of the sample preparation method on the measured value. 
BAS/1/1024-01-01
Graciela Gonzalez Gil
Membrane filtration, biofouling, chemical analysis
Chemical & Biological engineering
The student should critically evaluate the current method, and propose or develop improvements/alternatives.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Undergraduate
Water Desalination and Reuse Center
Tectonic evolution through analogue modelling
Academic Program: Energy Resources and Petroleum Engineering
The Arabian plate geology group is building a state of the art analogue modelling laboratory, which will be functional by year-end 2020. The apparatus consists of two rigid plates which can be displaced in x,y and z directions. This setup brings unlimited variety for future geological models. The analogue modelling apparatus can be used with either CT-scanner or normal laboratory conditions, for which an additional acquisition apparatus is under construction. This equipment will be used primarily for modeling large-scale tectonic processes of the Red Sea and the Dead Sea Transform.
BAS/1/1400-01-01
Jakub Fedorik (jakub.fedorik@kaust.edu.sa)
Structural geology, Analogue modelling, Arabian plate geology
Structural geology
Finalizing of construction of the photogrammetry apparatus, designing the analogue model setup, running experiments, analysis of data, PIV or structural interpretation and comparison to natural cases. 
Energy Resources and Petroleum Engineering
Physical Sciences and Engineering
Undergraduate
Ali I. Al-Naimi Petroleum Engineering Research Center
Body Area Networks
Academic Program: Electrical Engineering
Students will be engaged in design, simulation and evaluation of a body area network using on-skin communication modules. Accurate, real time sensing of body vital functions leads to better preventative medicine approached and more accurate diagnosis. State of the art wireless solutions, such as Bluetooth, attempt to provide such functionality, however their relatively large size and power demands limited their widespread use for personal telemetry. This project advocates the use of a new communication channel instead of air, which is the “human body” itself. Our vision is to provide a network of smart mini-distributed body mounted sensors that can perform personal telemetry and connect to the external world wirelessly using a central hub. The sensors themselves do require a radio frequency section or antennas since they directly interface to the skin (similar to smart watches) and hence will have a significantly reduced area, and use at least an order of magnitude less power, enabling a variety of network architectures and applications. The system will use very low power pulses for communication, well below what is required by health guidelines and exposure limitations. Distributing such small and smart sensors over the human body will allow users to monitor many vital signals, such as body temperature and electrocardiogram with unprecedented accuracy.
BAS/1/1686-01-01
Electrical Engineering ​
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Conceptual design of membrane processes
Academic Program: Chemical and Biological Engineering
The aim of this project is to simulate different membrane processes to develop rules of thumb for conceptual design of membrane processes. The student in this project will learn how to use the software Aspen Plus®, a wide used software in petrochemical industry, to simulate the membrane process for the separation of propylene and propane. Through this project, the student will learn all the basic knowledge of membrane processes, understand the reason why gas separation is so important in chemical industry and what are the advantages and challenges in the membrane studies. The education aim of the project is to stimulate the interest of the student in membrane studies.
BAS/1/1375-01-01
test@gmail.com
x
​Chemical Engineering
Please contact professor for details.
Chemical and Biological Engineering
Physical Sciences and Engineering
Graduate
Advanced Membranes and Porous Materials Center
Statistical models based on stochastic partial differential equations
Academic Program: Statistics
The student will learn about modern statistical methods based on stochastic partial differential equations (SPDEs). One important advantage with formulating statistical models using SPDEs is that it facilitates non-Gaussian extensions of several popular Gaussian models. Such extensions are useful for applications where the data has features that cannot be captured by Gaussian models. The goal of the project is to implement and compare these models for applications to longitudinal medical data and spatial environmental data.
BAS/1/1687-01-01
​Statistics
​​Written report and computer code that reproduces the results
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Multilevel and Unbiased Monte Carlo Methods for Option Pricing
Academic Program: Applied Mathematics and Computer Science
This project will focus on the numerical estimation of financial options. The latter are contracts associated to financial objects such as stocks, which can be mathematically expressed as expectations with respect to a diffusion process. In this project, we will develop Monte Carlo methods which can provide numerical estimates of these option prices, which are not available in closed forms. In particular, the diffusion processes will have to be time-discretized and we will use advanced multilevel and unbiased techniques to provide estimates sometimes with no time-discretization error.​
BAS/1/1681-01-01
​Computational and Numerical Methods
​Implementation and development of algorithms​
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Online Outlier Detection for Functional Data
Academic Program: Statistics
The student will learn state-of-the-art statistical methods for functional data analysis and spatial statistics, explore various approaches of functional data ranking and outlier detection, especially for images and surfaces, develop algorithms for online outlier detection with applications to spatial deformation detection and brain signal analysis.​
WBS BAS/1/1655-01-01
​Statistics, Data Science, Environmental Science, Computer Science, Applied Mathematics
​At the end of the internship, the student is expected to implement the designed application and submit a written report or a poster​
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Internship on Deep learning Methods for Satellite Data Downscaling
Academic Program: Statistics
The student will learn state-of-the-art statistical and machine learning methods for image super-resolution, explore various sources of satelliteimages and deploy experiments to manipulate and analyze large scale dataset.​
WBS BAS/1/1655-01-01
​Statistics, Data Science, Environmental Science, Computer Science,Applied Mathematics​
Statistics
Computer, Electrical and Mathematical Sciences and Engineering
Continual Learning
Academic Program: Computer Science
Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot accessdatafrom previous tasks and when the model has a fixed capacity. In this project, the goal is to develop and improve the capability of the machine learning methods not to forget older concepts as time passes.References[1] Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach,MohamedElhoseiny, Efficient Lifelong Learning with A-GEM, ICLR, 2019[2] Mohamed Elhoseiny,Francesca Babiloni, Rahaf Aljundi, ManoharPaluri,Marcus Rohrbach, Tinne Tuytelaars, Exploring the Challenges towards Lifelong Fact Learning, ACCV 2018https://arxiv.org/abs/1711.09601[3] Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, Tinne Tuytelaars, Memory Aware Synapses: Learningwhat(not) to forget, ECCV 2018https://arxiv.org/abs/1711.09601[4]Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, MarcusRohrbachUncertainty-guided Continual Learning with Bayesian Neural Networks https://arxiv.org/abs/1906.02425For more references, you may visit https://nips.cc/Conferences/2018/Schedule?showEvent=10910 https://icml.cc/Conferences/2019/Schedule?showEvent=3528​​​
BAS/1/1685-01-01
​Computer Vision and Machine Learning
​Develop a working research prototype for a continual learning approach1) students should learn about machine learning, deep learning, and the respective target application chosen for the internship. 2) students are expected to show capability to go from an idea to a working prototype; pushing the limits of what the state of the art can do.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Imagination Inspired Vision
Academic Program: Computer Science
Imagination has been the source of novel ideas that enable humanity to progress at an ever-faster rate. It is also is one of the key properties of human intelligence that enables us to generate creative products like music and art. Imagination is not only helpful for creating Art and Fashion, it also helps us see the world. The goal of this project is to focus on developing techniques that empower AI machines to see the world (computer vision)orto create novel products (e.g., fashion and art), hence the name Imagination Inspired Vision.Fore more context; see my talk on this topic, recorded at the University of Southern California; https://bluejeans.com/playback/s/jC9ksXIMhbHTw25sLghFgeGoWGNHcM 9VYc6QJQ8Z0yEq9X5aogZUWJ0AwDKz8hSR Vision-CAIR group at KAUST stands for Computer Vision- "C" Artificial Intelligence Research. "C" is for Content or Creative since we cover in our lab both Vision Content AI research and Vision- Creative AI research. For more information, please visit:http://www.mohamed-elhoseiny.com/vision-cair-group​​​​
BAS/1/1685-01-01
​Computer Vision and Machine Learning
​Develop imagination inspired solutions machine learning research for helping understanding and the creation of the unseen. 1) students should learn about machine learning, deep learning, andtherespective target application chosen for the internship. 2) students are expected to show capability to go from an idea to a working prototype; pushing the limits of what the state of the art can do.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Diel Variations in the primary productivity of the upper ocean from autonomous glider and autonomous profiling float observations
Academic Program: Marine Science
Using autonomous gliders and profiling floats, we have investigated the interaction of physical and biogeochemical processes in the upper layers of the Red Sea. In all of these, we see distinct diel patterns of variability inkeybiogeochemical variables that include chlorophyll, suspended particles (particulate carbon), and dissolved oxygen. These processes represent the complex interaction of the daily solar cycle, nutrient supply, and physical processes that contribute to horizontal transport and vertical mixing of the water column. The goal of this study is to understand the diel variations in the key biogeochemical variables and determine how they are informing us about the primary productivity of the upper layer. The visiting student will work with existing data sets that we have been obtaining since 2013, and will also be able to participate in ongoing work that is currently ongoing within the Red Sea.​​​
BAS/1/1032-01-01
​Biological Oceanography, Primary Productivity
​1. Understand the presence and persistence of diel variability inkey biogeochemical variables that have been obtained with autonomous platforms in the central and northern Red Sea.2.   Evaluate whether it is possible from these data sets to determine ifnetprimary production is occurring, or if the system is in a static, recycling state. 3. If the outcome of this effort is deemed successful, and meaningful, we expect to include this work within a publication in which the student can participate​.
Marine Science
Biological and Environmental Sciences and Engineering
Red Sea Research Center
Microfluidics-based single-molecule fluorescence imaging of nanoscopic cellular interactions
Academic Program: BioScience
The adhesion of cells to the endothelium occurs through spatio-temporally regulated interactions that are mediated by multiple intra- and inter-cellular components. The mechanism of cell adhesion has been investigated primarily using ensemble-based experiments, which provides indirect information about how individual molecules work in such a complex system. In this project, we develop microfluidics-based in-vitro live-cell single-molecule fluorescence imaging technique to unravel nanoscopic spatiotemporal interaction between adhesion molecules during the cell migration. Specifically, we aim to address some key questions in the initial step of hematopoietic stem cell homing mediated by selectin-ligand interactions.
BAS/1/1028-01-01
​Fluorescence microscopy, Micro/nano fabrication, Optics, Biophysics, Immunology
​Development of new microfluidics-based live-cell single-molecule fluorescence imaging technique.Characterization of nanoscopic spatiotemporal interaction between selectins and their ligands occurring during the cell migration.​
BioScience
Biological and Environmental Sciences and Engineering
Development of shortwave infrared emitting fluorophores and bioimaging application
Academic Program: BioScience
One of the main challenges in applying fluorescence microscopy to large biological specimens including tissues and whole organisms, which is the latest focus in fluorescence imaging field, is the absorption and scattering of both excitation light and fluorescence emission by the specimens. Oneofthe best ways to capture high quality images through thick biological specimens is to use shortwave infrared (SWIR, 1,000-1,700 nm) fluorescence since both absorption and scattering of light are highly suppressed in this wavelength region. However, fluorophores that are available for SWIR fluorescence imaging at the moment are very limited. In this project, we develop new SWIR emitting fluorophores using conjugated polymer materials. Through in depth photophysical characterization including single-molecule SWIR fluorescence microscopy, we aim to develop new SWIR-emitting conjugated polymer nanoparticles. Based on the photophysical characteristics of the developed materials, we also aimtodevelop new SWIR fluorescence imaging techniques including time-gated imaging.
BAS/1/1028-01-01
​Fluorescence microscopy, Optics, Physics, Cell biology
​Development of new SWIR-emitting conjugated polymer nanoparticles. Detailed photophysical characterization of the developed materials.  Optimization of SWIR fluorescence microscopy system for bioimaging applications.​
BioScience
Biological and Environmental Sciences and Engineering
Joint models for longitudinal and survival data
Academic Program: Applied Mathematics and Computer Science
Joint models are essential is most biomedical applications due to their ability to model two data types simultaneously. In this project, the aim istodevelop vignettes for the implementation of joint models within theR-INLAframework. The prospective candidate would gain coding skills in R and potentially develop new statistical methodologies to handle real-world phenomena within the context of joint models.​​
BAS/1/1667-01-01
​Statistics
​Vignettes for the implementation of joint models within the R-INLA framework​
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Impacts of UV radiation on corals and other organisms in the Red Sea
Academic Program: Marine Science
UV radiation has been identified as a key abiotic stressor in the marine environment, particularly in waters characterized by high optical transparency. One such environment is the Red Sea basin that, due to consistently minimal concentrations of UV-attenuating substances, has an exceptionally high transparency to UV radiation. This optical property and the proximity of the basin to the equator means that Red Sea organisms are exposed to intense, damaging levels of UV radiation.This project will investigate the effects of UV on scleractinian corals and other organisms in the Red Sea. ​
BAS/1/1072-01-01
​Marine Science
​Bibliographic search and bibliometric analysis.Design of field and laboratory experiments.Apply molecular laboratory techniques to evaluate UV effects on e.g. DNA damage, symbiosis state, lipid peroxidation, etc.Write final report summarizing the project work and key findings.
Marine Science
Biological and Environmental Sciences and Engineering
Red Sea Research Center
Biological stability of chlorinated and non-chlorinated drinking water
Academic Program: Environmental Science and Engineering
Drinking water is distributed from the treatment facility to consumers through extended man-made piping systems. The drinking water system should be microbiologically safe and biologically stable (WHO, 2006). The biological stability criterion refers to maintaining the microbial drinking water quality in time and distance from the point of drinking water production up to the point of consumption. This research will be conducted at the unique drinking water distribution system (DWDS) at KAUST a confined network of the same age supplied with reverse osmosis (RO) based drinking water. The aim of the project is to characterize temporal and spatial dynamics in biofilms and microbial community in the water from source to tap with the considerations of the impact of residual disinfectant use The results will allow better understanding whether residual chlorine is needed for distribution of RO produced drinking water and will lead to better insights on the biological stability of the produced water. ​
BAS/1/1024-01-01
​Chemistry, environmental science
Operate miniature drinking water distribution networks (preparation of solutions, setting up  and run equipment, problem solving) Sample analysis (biological and chemical analysis, DNA extraction, etc.Data analysis Written and oral presentation of (intermediate) results.​
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Characterization of biofilm growthrate in a membrane system
Academic Program: Environmental Science and Engineering
Membrane filtration plays an important role in seawater desalination and wastewater reclamation. Biofouling is an unacceptable loss of performance caused by the growth of bacteria inside the membrane element. The aim of the project is to establish relations between operational conditions, such as water velocity, production rate and nutrient concentration and the growth rate of biofilms. The results will allow better understanding and control of biofouling formation. The two main methods to observe the thickness of the biofilm are via its hydraulic resistance and via optical coherence tomography (OCT). ​
BAS/1/1024-01-01
​chemical engineering, environmental science, etc
​Formulate a research question and design the experiments accordingly.Run experiment (preparation of solutions, setting up and run equipment, problem solving, OCT-scan of biofilm)​Data analysisWritten and oral presentation of (intermediate) results.
Environmental Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Gradient compression for distributed training of machine learning models
Academic Program: Computer Science
Modern supervised machine learning models are trained using enormous amounts of data, and for this distributed computing systems are used.Thetraining data is distributed across the memory of the nodes of the system, and in each step of the training process one needs to aggregate updates computed by all nodes using local data. This aggregation step requires communication of a large tensor, which is the bottleneck limiting the efficiency of the training method.To mitigate this issue, various compression (e.g., sparsification/quantization/dithering) schemes were propose in the literature recently. However, many theoretical, system-level and practical questions remain to be open.In this project the intern will aim to advance the state of the art in some aspect of this field. As this is a fast moving field, details of the project will only be finalized together with the successful applicant.Background reading based on research on this topic done in mygroup: https://arxiv.org/abs/1905.11261https://arxiv.org/abs/1905.10988 https://arxiv.org/abs/1903.06701 https://arxiv.org/abs/1901.09437 https://arxiv.org/abs/1901.09269https://www.frontiersin.org/articles/10.3389/fams.2018.00062/abstract  https://arxiv.org/abs/1610.05492https://arxiv.org/abs/1610.02527​
BAS/1/1677-01-01
​computer science, mathematics, machine learning
​Ideally author or coauthor a research paper, and submit it to a premier conference in the field (e.g., ICML, AISTATS, NeurIPS, ICLR).​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Towards a Principled Understanding of Deep Learning
Academic Program: Computer Science
Deep learning models provide state of the art performance on many practical machine learning tasks. However, there is a large gapbetweenour theoretical / conceptual understanding and practice.The intern will work in one of the follow areas, depending on interest and background: - deep learning models- adversarial attacks and robustness- optimization for deep learning- generalization of deep learning- GANs- model compression
BAS/1/1677-01-01
​computer science, mathematics
​Ideally contribution to a research paper​.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Federated Learning
Academic Program: Computer Science
Federated Learning (FL) enables mobile phones to collaboratively learnashared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. This goes beyond the use of local models that make predictions on mobile devices by bringing model training to the device as well.FL was co-invented by my former student Jakub Konecny, myself and Google. We have launched. a FL system in 2017, it is now in use in more than 1 billion Android devices:https://ai.googleblog.com/2017/04/federated-learning-collaborative.html https://ai.google/research/pubs/pub45648 In this project we will investigate further improvements and applications of FL.​​​​
BAS/1/1677-01-01
​Machine Learning
​Ideally a joint research paper.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Topics in Machine Learning and Optimization
Academic Program: Applied Mathematics and Computer Science
Topics in machine learning (ML). The project can be of a theoretical nature (e.g., design of optimization algorithms for training ML models; building foundations of deep learning; distributed, stochastic and nonconvex optimization), or of a practical nature (e.g., creative application and modification of existing techniques to problems in federated learning, computer vision, health, robotics, engineering). The precise topic will be decided together with the successful applicants, and will be tailored to their skills and background.
ASP/1/1669-01-01
​Computer Science, Mathematics or a related discipline
​Original research – contribution to a research paper​.
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Mapping protein complexes in vivo
Academic Program: Plant Science
During multicellular development, the specification of distinct cell fates is often regulated by the same transcription factors operating differently in distinct cis-regulatory modules. In the model plant Arabidopsis root, distinct cell types are determined by the interacting transcription factors within one contiguous tissue layer. Using fluorescence lifetime microscopy we revealacell type-specific protein complexes that differentially regulate target genes and instruct distinct cell fates. In addition to determining the network function in other organs, we are currently studying how are these complexes established and maintained by generating a temporal interaction map and dissecting the transcriptional regulation by these complexes.We will use the following tools: FRET-FLIM, confocal microscopy imaging, mutants analysis.​
BAS/1/1081-01-01
​Developmental biology, cell biology
​Generate a spatio-temporal protein interaction map in vivo​
Plant Science
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
Molecular mechanisms underlying growth and defense in plants
Academic Program: Plant Science
When attacked by pathogens, plants allocate their energy and resources into defense responses at the expense of growth. Hence, understanding the mechanisms by which plants prioritize their responses is instrumental for improving plant defense and growth and consequently increasing crop yield. Recently, we have established a link between the defense hormone Jasmonic Acid (JA) and a transcription factor pathway with key roles in development. We have found that the developmental regulators BIRD proteins mediate Jasmonic acid defense response through interacting with core JA signaling pathway genes. The project aims to dissect the molecular framework underlying the dual function of these protein complexes. The identified signaling networks and pathways can then be rewired to allow simultaneous growth and defense. we will use the following tools: RNAseq, genome editing tools, fluorescence microscopy imaging amd a range methods for detecting protein-protein interactions in vitro and in vivo.
BAS/1/1081-01-01
​Developmental biology, cell biology
​The gained knowledge will be used to generate resistant crops with optimal growth behavior using genome editing tools.​
Plant Science
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
Spatio temporal analysis of expression of genes controlling assymetric stem cell division and tissue patterning in plants
Academic Program: Plant Science
BIRDs nuclear factors have been described to regulate root growth through association with the transcription factors SCARECROW and SHORTROOT, however their function in other organs remain to be elucidated. Here we propose to dissect network function in lateral roots and leaves. We will determine their physical associations spatially and during different developmental stages. We will also assess whether their target are regulated similarly.Objectives: In this project we aim to dissect how BIRD proteins regulate leave tissue patterning and map their localization in different mutant backgrounds. In addition, we will dissect binding sites in different target genes and alter specific binding by site directed mutagenesis. Technologies: confocal imaging microscopy, site directed mutagenesis, promoter activities using dual luciferase, plant phenotyping, cloning using gateway technology ​
ASP/1/1669-01-01
​Plant Biology​
​Map the expression of the genes at different developmental stages and dissect thein binding motifs
Plant Science
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
Experimental study of carbon-free combustion
Academic Program: Mechanical Engineering
A major goal of combustion research is to reduce emissions and minimize the harmful impact of energy production and transportation on the environment. Advancements in combustion sciences enabled strong reduction of NOX, SOX and particulates. The challenge for the next decade is reduction of carbon dioxide. One strategy is to completely remove carbon from the fuel, using carbon-free hydrogen carrier such as ammonia. Combustion of ammonia is not well understood, and no detailed information on the flame of ammonia-air flames is available. In this project the student will perform 1D Raman measurements of temperature and major species in ammonia flames in collaboration with a postdoc or a Ph.D. student. ​​​​​​
BAS/1/1388-01-01
​Mechanical, Aerospace, Chemical Engineering or Applied Physics
​The student will learn the fundamental of laser spectroscopy, and will gain hands-on experience in the operation of ammonia burners, and advanced laser diagnostics. He will acquire and analyze unique experimental datasets, and advance the understanding of combustion of ammonia-air flames.  ​
Mechanical Engineering
Physical Sciences and Engineering
Clean Combustion Research Center
Passivation in Perovskite Solar Cells
Academic Program: Materials Science & Engineering
Perovskites are an emerging class of materials which offers a high-efficiency photovoltaics owing to its appealing optical and electronic properties. However, defects such asnon-coordinated ions at their grain boundaries and surfaces do contribute to non-radiative photo-carrier recombination. This undesirably inflates theopen-circuit voltage deficit and is a likely contributor to the widely reported phenomenon ofhysteresis in the current-voltage characteristics which is set to remain at the forefront of thecontinuing progress towards to the theoretical PCE limit of 30%. In this project, the candidate will have an opportunity to explore some new interfacial engineering strategies, such as surface passivation, grain boundary passivation and contact passivation to eliminate the defects. This will help to increase the open circuit voltage of the perovskite devices which will be employed to achieve champion perovskite/silicon tandem solar cells. At the end of the internship, the candidate(s) will be experienced the full processing steps of the perovskite solar cells and they may have an opportunity to publish the findings in well-respected scientific journals.​​
BAS/1/1387-01-01
​Solar Cells
​Exploring new passivation moleculesTesting conductive polymers which has carboxyl groups for passivation purpose Testing 2D perovskites for interface passivation purposeAchieving >1.2V open circuit voltage for 1.6 eV perovskite absorbers based solar cells
Materials Science & Engineering
Physical Sciences and Engineering
KAUST Solar Center
Thermal Evaporation of Perovskite Absorbers for Silicon Perovskite Tandem Solar Cells
Academic Program: Materials Science & Engineering
Perovskite/silicon tandem solar cells are very promising technology to achieve >30% power conversion efficiency target. Despite this technology features promising efficiency potential, several challenges need to be overcome prior to possible industrial application. A particular challenge relates to the fact thatto maximize the light coupling into the bottom cells, the tandems require so-called random-pyramid textured bottom cells (with a feature size of several micron, see Fig. 1a). Whereas flat substrates have enormous reflection losses (Fig. 1c), textured substrates enable improved light coupling into the silicon wafers. Textured silicon solar cells are the industrial state-of-the-art, and there is likely little incentive to develop processes on expensive mirror-polished wafers. However, due to the characteristics of textured surfaces, which present irregularities and low-wetting properties, conventional solution techniques cannot be used to deposit the perovskite or the extraction layers.From this, it can be readily understood that the top cell fabrication needs to rely on scalable and conformal deposition methods, with a high degree of uniformity and reproducibility.  For these reasons, we aim in this project to alter our current state-of-the-art solution-based PSC processing procedures as much as possible towards scalable vacuum-based techniques. In this projects, the candidate will perform an evaporation of perovskites precursors and the conversion of the layers will be performed either by co-evaporation or vacuum/solution hybrid based methods. At the end of the internship period, students will learn how to fabricate and optimize perovskite solar cells. The candidate will have a chance to experience the fabrication of fully textured silicon/perovskite tandem solar cells.Why thermal evaporated perovskites?Scalability: vacuum based deposition techniques are a well-established technique in several industrial processes.Reproducibility: thermal evaporation process of perovskite absorbers is not affected by the multitude of uncontrollable parameters typical for solution-processed perovskites.Uniformity: unlikely solution deposition, evaporation enables uniform deposition, independently from the substrate type.Low thermal budget: thermal evaporation is a low-temperature process and the substrate can be eventually further cooled to room temperature.Environmentally benign: evaporation does not involve toxic solvents.Process flexibility: different perovskite composition can be obtained by evaporation, according to the desired application. ​
BAS/1/1387-01-01
​Solar Cells
​Thickness control of the evaporated lead iodideOptimization of co-evaporated of lead iodide and cesium bromide Process sophistication for triple cation system perovskites>23% tandem solar cell efficiency using thermal evaporated perovskites​
Materials Science & Engineering
Physical Sciences and Engineering
KAUST Solar Center
Broadband Transparent Front Electrodes for Perovskite Silicon Tandem Solar Cells
Academic Program: Materials Science & Engineering
This project is focusing on developing novel transparent electrodes for perovskite/silicon tandem solar cells whichare anemerging class of solar cells technology. Perovskite/silicon tandem solar cellsaim to achieve the power conversion efficiencies beyond the single junction limit of the silicon solar cells. Towards this end, developinghighly transparent electrodes are critical for the maximized light harvesting since parasitic absorption loses originating from the transparent conductive oxides causes drastic current losses. In this project, the deposition of the novel transparent conductive oxides will be performed the sputtering technique. The candidate will gain experience on the structural, optical and electrical and characterization of the thin films. Moreover, the candidate will have experience in the fabrication and characterization of the perovskite/silicon tandem solar cells to test the developed materials. Why we need broadband transparent electrodes on perovskite/silicon tandem solar cells?In perovskite/silicon tandem solar cells, the perovskite top cell efficiently harvests the blue part of the solar spectrum, while transmitting the red part, which is absorbed in the silicon bottom cell. In this way, the tandems can overcome the single-junction efficiency limit of silicon solar cells. However, parasitic absorption losses originating from the transparent electrodes hinders the light harvesting. Typically, a front transparent electrode in any solar cell has a sufficiently large (>3 eV) optical band gap to avoid absorption losses in the visible range of the spectrum. This requirement is already fulfilled by several TCOs such as ITO, IZO, and IO: H. For perovskite/silicon tandem solar cells, transparent electrodes with low absorptance in the NIR‐IR part of the spectrum are required since these devices use optical absorbers that are active in this range of the spectrum. Towards reaching >30% photoconversion efficiency, designing transparent electrodes with exceptionally large broadband transparency carriers critical importance. References[1] Morales‐Masis, M., De Wolf, S., Woods‐Robinson, R., Ager, J. W., & Ballif, C. (2017). Transparent electrodes for efficient optoelectronics. Advanced Electronic Materials, 3(5), 1600529.​
BAS/1/1387-01-01
​Solar Cells
​Optimization of the sputtering parameters of H:In2O3 and IZO transparent electrodesOptical and electrical characterization of the fabricated films Achieving <20 ohm/sq. sheet resistivity for 1000 nm thickness together with broadband transparency (<10% absorbance within this region).Exploring new TCO by metal doping of In2O3​
Materials Science & Engineering
Physical Sciences and Engineering
KAUST Solar Center
Novel Electron and Hole Transport Layers by Atomic Layer Deposition Technique for Perovskite Silicon Tandem Solar Cells
Academic Program: Materials Science & Engineering
The atomic layer deposition (ALD) technique is an efficient technique to deposit thin films at low temperature, which is based on self-limiting surface reactions by exposing sequentially on the substrate with various precursors and reactants. It provides excellent control over film thickness at the angstrom or monolayer level and deposition on high aspect ratio nano andmicrostructures with excellent step coverage. To exploit these advantages, we are employing ALD deposited electron and hole transport layers on randomly textured silicon wafers. In this project, to increase the light coupling in the tandem solar cells, the deposition of the broadband transparent electron and hole transport layers will be performed by the ALD technique. The candidate will gain experience on the structural, optical and electrical and characterization of the thin films by optimizing the deposition recipes. Moreover, the candidate will have experience in the fabrication and characterization of the perovskite solar cells and will have an opportunity to experience the fabrication of perovskite/silicon tandem solar cells. Why Silicon/Perovskite Tandems?The current global photovoltaics market (nowadays taken for more than 90% by crystalline silicon solar cells) has seen a sustained growth of its production capacity by more than 20% annually [1]. From a longer-term perspective, the market is expected to make a transition towards ultra-high efficiencies. For this, further efficiency improvement s will be needed, beyond the single-junction efficiency limit of silicon [2].  The most straight-forward way to do so is in the form of a silicon-based tandem solar cell, where a wider-bandgap top cell overlays the silicon bottom cell. In perovskite/silicon tandem solar cells, the perovskite top cell efficiently harvests the blue part of the solar spectrum, while transmitting the red part, which is absorbed in the silicon bottom cell. In this way, the tandems can overcome the single-junction efficiency limit of silicon solar cells. References[1] Haegel, Nancy M., et al. "Terawatt-scale photovoltaics: Trajectories and challenges." Science 356.6334 (2017): 141-143. [2] Richter, A., Hermle, M., & Glunz, S. W. (2013). "Reassessment of the limiting efficiency for crystalline silicon solar cells." IEEE Journal of Photovoltaics, 3(4), 1184-1191.​​​
BAS/1/1387-01-01
​Solar Cells
​Optimization of SnO2 thin films for perovskite solar cells applicationsTesting these layer for flat junction opaque devices for performance analysis Achieving >23% tandem solar cells efficiency using these buffer layers.Testing new ALD precursors for electron and hole transporting purposes
Materials Science & Engineering
Physical Sciences and Engineering
KAUST Solar Center
Salinity Tolerance of Plants
Academic Program: Plant Science
Soil salinity is a major abiotic stress constraining crop production. We are investigating how some plants are able to cope with salt stress, to then inform research on other crops to make them more tolerant to salinity stress. Quinoa (Chenopodium quinoa) tastes good, is highly nutritious and is a very salt tolerant crop; however, we are yet to discover the mechanisms for its high salt tolerance. This is one species that we are currently studying. We are also looking at mechanisms of tolerance in wild relatives of domesticated crops, in particular wild tomatoes and wild barley.​​
BAS/1/1038-01-01
mark.tester@kaust.edu.sa
Salinity
​Biology, Computer science
​Lots of good research / lots of hard work and fun.​
Plant Science
Biological and Environmental Sciences and Engineering
Undergraduate
Center for Desert Agriculture
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
Inverse Problems in Imaging
Academic Program: Computer Science
Inverse problems are abundant in the field of imaging, and range from simple image processing tasks such as denoising and deblurring to full-scale reconstruction problems like computed tomography (reconstructing 3D volumes from 2D projections). The purpose of this internship is to learn about inverse problems, and critical techniques for solving them, including convex and non-convex optimization, sparse coding, and compressive sensing.​
BAS/1/2902-01-01
​Computer Science, Applied Mathematics.​
​This project requires some familiarity with basic numerical methods as well as programming skills. Close collaboration with other team members is expected. Possibility for co-authoring a scientific article in a conference or journal.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Computational Cameras
Academic Program: Computer Science
Computational cameras are imaging systems that are combinations of optics, electronics, and algorithms that jointly enable new approaches to image sensing. Computational imaging systems are of interest for applications such as polarization imaging, hyperspectral cameras, time-of-flight and depth imaging, light fields, high speed cameras, or cameras with small and exotic form factors. The internship will be to learn about computational imaging approaches, and work in an interdisciplinary team to develop new camera systems in one of the application domains.​
BAS/1/2902-01-01
​Computer Science, Electrical or Optical Engineering
​Depending on the background of the student, the work can be more optics oriented or more software oriented. Close collaboration with other team members is expected. Possibility for co-authoring a scientific article in a conference or journal.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
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.
BioScience
Biological and Environmental Sciences and Engineering
Fouling in Membrane Filtration Systems
Academic Program: Earth Science and Engineering
In the last decades the use of membrane systems for fresh water production has increased strongly to supply the growing water demand due to increasing human population, industrial and agricultural activity, economic growth and urbanization. Fouling represent one of the major drawbacks of membrane systems. In this project, we aim to explore different techniques in order to study the fouling developed in the system and relate with the membrane performance decrease. ​
ASP/1/1669-01-01
​Environmental science and engineering
​The student will learn various approaches to characterize the fouling developed in membrane filtration systems. He/She will learn different techniques (i.e. Confocal, Flow Cytometry, ATP, SEM, LCOCD etc). Several experiments will be run in order to relate the fouling developed in the system with the overall performance.
Earth Science and Engineering
Biological and Environmental Sciences and Engineering
Water Desalination and Reuse Center
Complex optoelectronics materials and phenomena
Academic Program: Electrical Engineering
Design, fabrication and characterization of new complex materials and phenomena for different optoelectronics applications​. ​​
BAS/1/1628-01-01
​Physics or Engineering or Chemistry
​Learning of different nanofabrication techniques; assembly of complex materials​ structures for different photonics applications
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Cutting-edge research on materials, devices, or physics of the third-generation semiconductor
Academic Program: Electrical Engineering
The wide bandgap semiconductors, also known as the third-generation semiconductors, have enormous potentials to revolutionize almost every industry on the planet and in space. Because of the unique and superior properties, they can be made in ultra-efficient, ultra-sensitive and ultra-reliable optical and electronic devices. Despite being in the early phrase of development and commercialization, the third-generation semiconductors have already resulted in industries worth of hundreds of billions USD and creating numerous jobs, as well as 2014 Nobel Prize in Physics. Opening your phone, computers and anything using electricity, you will likely find plenty of them. More potentials are to be unlocked by smart and hard-working researchers such as you. This VSRP project is offered by the Advanced Semiconductor Laboratory, a world-leading laboratory with state-of-the-art facilities in the third-generation semiconductor research. The VSRP students will have the opportunity to learn the latest materials, devices, and physics of the third-generation semiconductor as well as the combination with other exciting things such as 2D materials and quantum photonics. More importantly, every VSRP student will have his/her own project to solve a key problem. The project needs 4-6 months to be completed. It is going to be an exciting period with intense training and research work both theoretically and experimentally. Successful students can likely publish their results in prestigious scientific venues. ​ ​​​​​​​
BAS/1/1664-01-01
​Electrical/Electronic Engineering, Physics, Material Science, Chemistry
​In research, no deliverables can be compared with a patent or a peer-review paper. In the past, all the VSRP students can generate patents or publish first-authored or co-authored papers in prestigious journals. The publication record has help strengthened their credentials greatly for future career development. Therefore, the incoming VSRP students are expected to do the same. PS: one example can be found here: https://goo.gl/8sorGf​
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Plant-Beneficial Microbe Interaction
Academic Program: BioScience
Abiotic stresses are the most important factors for hampering plant growth and yield world-wide. However, beneficial microbes can help plants to enhance stress tolerance of plants. DARWIN 21 is a large scale project to isolate and study the interaction how rhizophere microbes contribute to enhance the capacity of plants under the most difficult abiotic stress conditions (http://www.darwin21.net/index.htm).In this project, the student will characterize several rhizosphere microbes and investigate whether they can confer plant resistance to different abiotic stresses. The student will learn how to apply techniques in microbiology, molecular biology and plant biology.​​
ASP/1/1669-01-01
​Microbiology, Genomics, Plant biology, Bioinformatics
​Isolation and characterization of bacterial strains. Analysis of beneficial microbes on plant physiology. Sequencing and bioinformatics to analyze microbial genomes, transcriptome and proteome analysis of beneficial microbes and plants.
BioScience
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
An iPSCs-based approach to model Type Two Diabetes in-vitro
Academic Program: BioScience
Studying the transcriptional and epigenetic mechanisms dysregulated in patients affected by metabolic disorders such as insulin resistance (IR) and type 2 diabetes mellitus (T2DM) is essential to derive efficient pharmacological approaches. We are seeking an outstanding student to work on a project focused on the study of the role of histone modifiers to the onset of metabolic disorders.​​​​​​
BAS/1/1077-01-01
​Molecular and Cellular Biology and/or Bioinformatics
​The selected candidate will use human stem cells and terminally differentiated glucose sensitive cell types and will acquire skills in molecular biology techniques including Chromatin Immuno-precipitation (ChIP), quantitative real-time PCR (Q-PCR) and next generation sequencing (NGS).​
BioScience
Biological and Environmental Sciences and Engineering
Screening for Carotenoid-Derived Signaling Molecules
Academic Program: BioScience
The project focuses on novel signaling molecules involved in plant development and response to environmental stress. It includes studies on the activity of selected carotenoid-metabolizing enzymes and the identification of their enzymatic products. Biological activity of products will be investigated by developmental assays using Arabidopsis and rice and by determining the effect of these compounds on the transcript levels of selected genes including strigolactone biosynthesis genes. These studies will be complemented by geno- and phenotyping of mutants disrupted in the corresponding genes. ​
ASP/1/1669-01-01
​Plant Biochemistry and Development
​Identification of new bioactive compounds/Better understanding of the regulation of strigolactone biosynthesis. Significant contribution to a publication 
BioScience
Biological and Environmental Sciences and Engineering
Center for Desert Agriculture
Machine Learning for Graphs
Academic Program: Computer Science

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

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

Challenges and sub-projects include:-         

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

-Analyze networks embedded in hyperbolicspace

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

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

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

-Search and propagation in multi-layernetworks

-Alignment of several but different real protein interaction networks​

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

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

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

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

ASP/1/1669-01-01
jesper
bio
​Computer Science, Applied Mathematics
​Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. We expect you(a)tobringenthusiasm,creativity,andhardwork,(b)givelabseminarsonyourwork, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. You will also get a good hands-on perspective at the frontier of machine intelligence and its applications in an interdisciplinary research group andenvironment​.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Graduate
Algorithmic Information Theory for Machine Intelligence
Academic Program: Computer Science
We recently developed numerical and computational techniques to use algorithmic information theory (AIT) to the analysis of networks. For publications see google scholar(https://scholar.google.com/citations?hl=sv&user=_DUppAgAAAAJ&view_op=list_works&sortby=pubdate).Subprojects include to-         Develop python packages for AIT analysis of large-scalenetworks-         Develop new AIT network embedding algorithms-         Analyze Convolutional Networks a representational learning usingAIT-         Quantify and benchmark AIT network analysis with othertechniques-         Large-scale computation of AIT networks using a supercomputer(Shaheen)Newand improved numerical approximation of algorithmic complexity using massive computations of Turing Machines on Shaheen(supercomputer)
ASP/1/1669-01-01
​Computer Science, Applied Mathematics
​Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report. In return this facilitates your critical thinking, presentations skills, and scientific writing. Your research, in collaboration and with support of team members, may lead to scientific publications. You will also get a good hands-on perspective at the frontier of machine intelligence and its applications in an interdisciplinary research group and environment.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Learning Generative Causal Models from Sparse Temporal Observations during Cellular Reprogramming
Academic Program: BioScience
​Recent work on stem cells and different mature specialized cells in different systems/organs (neurons, blood cells,) has revealed a stunning plasticity and capacity of reprogramming cells. For example, mature cells can be reprogrammed into pluripotent stem cells, and exciting work on engineered design of tissues and organs (organoids) are underway. On the one hand the community has since the sequencing of the human genome produced very efficient tools to read off the corresponding molecular events accompanying reprogramming and engineering of cells. Recently, the discovery of the CRISPR techniques has equipped us with unprecedented opportunities for precise writing or editing of the genomes. These developments in fundamental biology and biotechnology are currently opening new tools and perspectives of vital significance for drug development, regenerative medicine, synthetic biology, and personalized medicine. Yet, in essence all these efforts require and would be greatly facilitated if we could advance from correlative data-analysis to a predictive discovery of which interventions (edits, engineering) are producing which effects. Thus, we are facing the fundamental problem on how to discover causal relations from data, or in other words, can we derive quantitative predictive laws fromdata?We offer internships forseveral highly motivatedbachelor (B.Sc.) ormaster (M.Sc.) students who will explore this fundamental question primarily from a computational standpoint. This includes using high-performance simulations of dynamical models, and design of algorithms in a controlled in-silico environment. For example, to identify (a) efficient algorithms for generation of ensembles of dynamical models, (b) use supervised deep learning algorithms for pattern discovery in large-scale simulation data-sets, (c) to perform deep data-driven analysis of computational models in biology, (d) pursue investigations of transfer entropy and related techniques for system identification. These tools will be tested utilizing rich and recent molecular data on cellular reprogramming.​​ ​​​​
BAS/1/1078-01-01
​computer science, mathematical modeling, machine learning, systems biology, bioscience
​Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. The project is suitable for candidates fascinated by dynamical causal systems, be it computational or those we find in the natural world, i.e. living cells. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. We publish avidly in both bioscience and computational sciences, not for the fame but rather as steps aiming to and motivated both by our quest of asking fundamental questions of relevance to human nature and discovery of transformative intelligent technologies inspired from nature. You will get a good hands-on perspective on the frontiers in dynamical systems and bioscience using state-of-the-art simulation and machine learning tools.
BioScience
Biological and Environmental Sciences and Engineering
Deep Learning and Machine Intelligence for Single Cell Genomics
Academic Program: BioScience
Single cell biology and genomics in particular are currently transforming the biosciences. Single cell RNA sequencing (scRNAseq), method of the year 2013 (Nature Methods), has now matured and large amounts of scRNAseq are now available. These data, characterizing living systems at an unprecedented level of resolution, hold the promise to set the stage for a fundamental quantitative understanding of living systems with special reference to genomic regulation and collective computation. Yet, there are a number of open problems on how to think about these data and how to pragmatically analyze them.In parallel, we have witnessed a rapid development in machine learning. The rise of computation, such as supercomputers (shaheen@KAUST) and GPU based techniques, in conjunction with data explosion (often referred to as big data), has fuelled the development of new techniques aiming for machine intelligence. In particular, techniques inspired from livings systems, such as deep convolutional networks, currently experience a renaissance. Driving forces include not only data and computation but also the availability of suite of open source platforms (e.g. Theano, Caffe, Torch7, TensorFlow) supporting machine-learning algorithms. These algorithms represent industry standard for processing images, speech, text, and runs on the majority of services and devices provided by Google, Amazon, Facebook, to name a few big players, as well as a numerous startups.We offer internships for several highly motivated bachelor (B.Sc.) or master (M.Sc.) students who will identify (a) appropriate supervised deep learning architectures and training algorithms for scRNAseq data, (b) explore generative adversarial network (GANs) techniques for estimation of high-dimensional data distribution in the single cell gene expression space. This work will be used to develop new techniques and to address open problems in single cell genomics such as pseudo-temporal ordering of single cell data, clustering of data, investigate representations, transfer learning, and unsupervised feature discovery. ​​​
BAS/1/1078-01-01
​computer science, bioscience, machine learning, systems biology, artificial intelligence​
​Individual projects will be tailored and narrowly designed from the above palette according to interest of the student, technical proficiency, and level of study. The project is suitable for candidates fascinated of living systems, interested in cutting edge bioscience, and artificial intelligence for science and not for discovering cats in YouTube. We expect you (a) to bring enthusiasm, creativity, and hard work, (b) give lab seminars on your work, and (c) produce a final written report.In returnthis facilitates your critical thinking, presentations skills, and scientific writing.Yourresearch, in collaboration and with support of team members, may lead to scientific publications. We publish avidly in both bioscience and computational sciences, not for the fame but rather as steps aiming to and motivated both by our quest of asking fundamental questions of relevance to human nature and discovery of transformative intelligent technologies inspired from nature. You will also get a good hands-on perspective at the frontier of bioscience and machine intelligence in an interdisciplinary research group and environment.
BioScience
Biological and Environmental Sciences and Engineering
Brain Inspired Computing
Academic Program: Electrical Engineering
Diagnostics become more important in third world countries as the people have limited access to medical care systems and have less awareness of healthy lifestyles. There is certainly a need for on-site detection in the life science fields; and for point-of-care diagnostics in rural areas of underdeveloped countries so that even an unskilled person can use the device to determine the presence of disease-causing markers. Currently, diagnostics commonly employ long assay time, trained personnel, sophisticated instruments, and require financial support. A good approach to overcome this current situation would be the use of flexible and paper-based point-of-care devices to detect specific biomarkers. Biomarkers provide insight into normal biological processes, pathogenic processes, and pharmacological therapeutic interventions. Hence, the development of more compatible, reliable, convenient, simple, easyto- use systems would be of great use to a person less skilled in medical diagnostic procedures.​​​​​
BAS/1/1605-01-01
​Electrical Engineering, Computer science, physics, neurosciences
​1-      A complete biosensor design and simulation  2-      Potential fully operational Hardware device  3-      Full detailed report on the design and participation in manuscript and papers writeup​
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Biomedical Sensors
Academic Program: Electrical Engineering
Conventional computing based on Von Neumann architecture has been shown to be approaching its limits in scalability and power consumption. If solved with contemporary machines, today’s applications in science and industry related to data analysis, pattern recognition and prediction would demand a huge computing power. In the era of ubiquitous sensing and data acquisition, a way to cheaply and power efficiently make sense of the collected ‘big data’ is of utmost importance. Here, human brain’s efficiency becomes the ultimate standard and inspiration for any future technology. Such trend of understanding the brain behavior is currently gaining a huge attention worldwide. At the sensors lab, students under the supervision of Prof. K.N. Salama are exploring new computing technologies miming the way our brains process and store data.​​​​
BAS/1/1605-01-01
​​Electrical Engineering, Computer science, physics, neurosciences
 Report of state of art brain inspired computers;   Implementation of state of art; Exploration of neuromorphic architectures;  Simulation and comparison of various alternatives​. ​
Electrical Engineering
Computer, Electrical and Mathematical Sciences and Engineering
Development of a novel Stimulated Raman Scattering microscopy system
Academic Program: BioScience
Microscopy techniques based on vibrational spectroscopy are poised to be part of the next generation of microscopes for biological applications based on their unique chemical contrast and sub-cellular resolution for non-invasive, non-destructive and label free imaging of biological samples as live cells. The project will focus on the development of a fast and low-noise detection system in a setup for microscopic vibrational spectroscopy based on Stimulated Raman Scattering, which is one of the most advanced and sensitive methods for label-free microscopy for bio-imaging. The system will be applied to vibrational imaging of cancer stem cells to unveil their specific bio-chemical signatures. ​ ​​​​
ASP/1/1669-01-01
​Electrical Engineering, physics
​Learn Coherent Raman Scattering techniques. Design, assemble and test circuitry for multiplexed and low-noise detection in a Stimulated Raman Scattering microscopy setup based on femtosecond broadband laser sources. Demonstrate fast and high S/N ratio imaging with multiplex (broadband) Stimulated Raman Scattering microscopy. ​ 
BioScience
Biological and Environmental Sciences and Engineering
Novel Micro-optical structures on optical fiber tip with two-photon lithography
Academic Program: BioScience
Optical fibers are nowadays an ubiquitous core element of telecommunication systems, new laser technologies and biomedical devices. Manufacturing techniques for optical fibers have been developed and refined in order create manifold geometries and optical properties (e.g.Dual clad fibers, fiber bundles, Photonic Crystal Fibers, to name a few). Yet the capability to fabricate complex miniaturized structures integrated with optical fibers to realize important optical functions (like beam shaping, beam deflection, fiber optical tweezers, etc.) has been demonstrated only very recently.The project will focus on the fabrication of optical wave-guiding structures on the tip of optical fibers exploiting to flexibility, resolution and 3D fabrication capability of Laser Direct Writing based on Two-Photon Lithography (TPL).​ ​​​​
ASP/1/1669-01-01
​Electrical Engineering, physics
​Learn Two-Photon Lithography. Design structures using wave-optics propagation software. Fabricate structures on optical fibers. Measurements to assess optical function of fabricated structures.​
BioScience
Biological and Environmental Sciences and Engineering
Wireless Networks for Better Smart Buildings
Academic Program: Computer Science
Smart building involves a construction of multiple layer system that is capable to sense, acquire, analyze and perform the proper action to maintain the operation of certain city entity. The layered system incorporates six layers including: sensing layer, network layer, software layer, semantic discovery layer, processing and reasoning layer, and service layer. All the statistics show that there will be a large demand for smart buildings in the future as it has the potential for energy saving, indoor services such as tracking, mapping, localization, identifying products, and downloading relevant information are the main drive for future trends. The number of smart devices is expected to be x10 of the current number in 2020. Efficient Resource allocation for context aware based systems with the future demand for smart devices is challenging. Thus, it is essential to utilize techniques such as data mining and machine learning for data analytics, reasoning, and decision-making in order to maximize the smart building operation efficiency and overcome all the related challenges.​​
BAS/1/1601-01-01
​Computer Science
​The student will consider smart building services as a core for gathering data. Such data can be collected and transmitted in several ways. However, we will be interested in wireless technologies as a platform using simple devices such as smart phones. We gather the data and use data mining and machine learning techniques to make the right decisions such that we guarantee the sustainability of the smart building services.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Realtime transmission in Underwater Wireless Environments
Academic Program: Computer Science
Design and implement a system that facilitates realtime data transmission in underwater wireless networks. This includes examining the data transmission over several types of water conditions along with extracting the data transmission characteristics.  ​​
BAS/1/1601-01-01
​Computer science and/or electrical engineering
​The student is expected to closely work on an underwater wireless optical transmission testbed. Using the testbed equipment’s, the student will be running several sets of experiments and collect certain data (e.g., bit error rates, frame rates, channel condition, ..etc). The extracted data will be later used to design an optimal resource allocation framework for facilitating realtime transmission in such environments. ​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Wireless Sensor Node Analysis Employing Energy Harvesting
Academic Program: Computer Science
The project consist of a theoretical and a system research thrusts in energy-harvesting wireless sensor networks. Looking at the energy harvesting module in sensor node from the system’s prospective, we observe that adopting an interruption/harvesting policy enhances the energy consumption, but it also increases average packet end-to-end delay and packet dropping! There are various tradeoffs exist in wireless sensor network (WSN) design. Of particular interest to the project are 1) End-to-end delay vs. energy harvesting & network size, and 2) Increasing network size allows for a smaller number of data sink nodes and reduces dropping but it also increases average end-to-end delay. In order to quantify the tradeoffs, we raise a question. How delay, network size, and harvesting policy (service vacation) interact with each other? In other words, how large the network dimensions can go to considering certain packet latency threshold and dropping? Energy harvesting module can arbitrary be triggered, upon empty buffers, and thus, imposes random interruption periods on the sensor node, these random cycles (vacations) increases latency due to residual vacation time that is consumed for harvesting. In order to solve the above challenge, the student will study this system and think of a theoretical and a system approaches (with the assistance of the instructor) to improve the end-to-end delay. For instance, a possible solution would be, instead of arbitrary harvesting time, we aim at optimizing this value to minimize the delay and dropping. We also aim at adding a constraint that forces the sensor node to trigger the harvesting phase once its battery is low! Currently, the networking lab has a set of 20 sensor motes that are programmable using TinyOS and also has all the necessary mathematical packages to evaluate the student proposed models. ​​​
BAS/1/1601-01-01
​Computer science and electrical engineering
​The student is expected to achieve:1.      (theatrical component): An optimization of the energy harvesting value that minimizes the system end-to-end delay validated using matlab. 2.      (systems component): Translate the results from (1) into a working code, which can be tested over our TelosB sensor motes using TinyOS (C/C++).   The above results, if completed, are considered novel and can result into a publication with the agreement with the instructor.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
A domain- specific language and code generation for Riemann solvers
Academic Program: Applied Mathematics and Computer Science
Riemann solvers are the core algorithms in numerical methods for wave propagation. They are also the most complex part of developing code for new applications. The design and implementation of an effective approximate Riemann solver typically requires substantial expertise and time. However, there exist generic approaches that utilize relatively straightforward properties of the hyperbolic system and are reasonably efficient. The goal of this project is to develop a domain- specific language for hyperbolic conservation laws and Riemann solvers, and implement automatic generation of efficient solvers based on a purely symbolic representation of the problem.  ​​​​​
BAS/1/1616-01-01
​Mathematics, computer science or engineering
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Laser-based Sensor Design and Development
Academic Program: Mechanical Engineering
The project involves design, development, and implementation of a laser-based optical sensor. The sensor will be used to monitor environmental pollutants (e.g., NOx, CO) or greenhouse gas emissions (e.g., N2O, CH4, CO2). The student will work on studying the electromagnetic spectrum of various molecules, choosing the candidate optical transitions, setting up the laser-based optical setup, and performinglaboratory measurements to detect species concentration in trace quantities. Advanced sensing strategies, such as wavelength modulation, cavity-enhancement and frequency combs will be utilized. The student will gain expertise in spectroscopy,statistical thermodynamics, machine learning, optical engineering, and mechanical design.​​​
BAS/1/1300-01-01
​Physics, Electrical Engineering, Mechanical Engineering, Chemical Engineering
​Spectral line selection using state-of-the-art spectroscopicmodels Design of optical, electronic and mechanical components of thesensor Laboratory validations of the sensorperformance ​Field testing for trace gasdetection​
Mechanical Engineering
Physical Sciences and Engineering
Clean Combustion Research Center
Future Fuel for Advanced Combustion Engines
Academic Program: Chemistry
Increasing focus on global warming and CO2 emsisions is pushing the engine technology to new frontiers. The overarching objective is to increase the efficiency of internal combustion engines so as to minimize the CO2 emissions. Advanced engine technologies revolve around compression ignition concepts but diesel is not deemed to be suitable fuel for achieving higher efficiency and low pollutant levels. In this context, we are exploring new fuel formulations which can be produced at a lower cost from the refinery but can provide superior performance in the engine. The project will involve exploring the ignition and emission characteristics of such candidate future fuels.​​​​​
BAS/1/1300-01-01
​Mechanical Engineering, Chemical Engineering, Chemistry
​Perofrm detailed physical/chemical characterization on select refinery stream fuels;Carry out ignition experiments in idealized reactor configurations of shock tube and rapid compression machine; Perform chemical kinetic modelling to develop surrogates for the fuels; Analyze the performance of new fuel in engine simulations; Recommend the optimal fuel formulation suitable for advanced compression ignition engines​.
Chemistry
Physical Sciences and Engineering
Clean Combustion Research Center
Chemical Kinetics of Novel Biofuels
Academic Program: Mechanical Engineering
Biofuels are becoming increasingly important in the world's energy infrastructure and their use has steadily been increasing in Europe and the U.S. With a number of candidate biofuels, a concentrated effort must be spent on choosing the optimal fuel that can be produced in a cost-effective manner and deliver the best performance within the engine. The student will initially work on choosing a few candidate biofuel molecules for investigating experimentally in the laboratory. Thereafter, he/she will perform a series of experimental studies on the selected fuels to understand their chemical kinetics behavior.​​​​​​​​​
BAS/1/1300-01-01
Mechanical Engineering or Chemical Engineering
- Perform life-cycle-analysis to select candidate biofuels - Characterize various biofuel molecules in terms of their physical properties - Conduct detailed experiments on the chemical kinetics behavior of these biofuels using shock tube and rapid compression machine - Run validated chemical kinetics models to estimate the performance of studied biofuels in an engine - Show how the new fuel leads to improvement in engine efficiency and reduction in emissions ​​​​
Mechanical Engineering
Physical Sciences and Engineering
Clean Combustion Research Center
Theoretical and numerical study on complex materials
Academic Program: Applied Mathematics and Computer Science
​The students are required to perform theoretical and numerical studies on wave propagation in artificial structures with complex structures. The contents include but are not limited to Fano resonance, absorption, trapping of electromagnetic or acoustic waves. ​​​​​
BAS/1/1626-01-01
​Physics, Mathematics, Material Sciences, and related
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Nonlinear Partial Differential Models
Academic Program: Applied Mathematics and Computer Science
In this project we will study some nonlinear partial differential equations models that arise in applications ranging from population dynamics, mean-field games, quantum chemistry and mechanics, medicine, quasi-geostrophic flows, and water waves.​​​​​
11111.0000000000
Mathematics or related field
The objective of the project is to study in detail modeling, analytical and numerical aspects of partial differential equations from concrete applications. A final report and presentation will be required. The work will be developed and the guidance of Prof. Diogo Gomes and the Research Scientist Dr. Saber Trabelsi.​
Applied Mathematics and Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Classification of long non-coding RNAs
Academic Program: Computer Science
​Long non-coding RNAs (lncRNAs) have been found to perform various functions in a wide variety of important biological processes. To make easier interpretation of lncRNA functions and conduct deep mining on these transcribed sequences, it is important to classify lncRNAs into different groups. lncRNA classification attracts much attention recently. The main technical difficulties are 1) the limited number of known lncRNAs (small training sample size), and 2) the very different lengths of lncRNAs. This project is to apply and further improve the string kernel algorithms developed in Prof. Gao’s group to the lncRNA classification problem. ​​​​​​​
BAS/1/1624-01-01
​Computer science, bioinformatics, electrical engineering, applied mathematics​
The visiting student for this project is expected to finish the following deliverables:1.      Give a throughout literature review on lncRNA classification methods and potential machine learning methods that can be applied to this problem. 2.      Get familiar with the string kernel algorithms developed in Prof. Gao’s group. 3.      Gather an lncRNA dataset to be used as the benchmark set for this research. 4.      Conduct a comprehensive comparative study of the state-of-the-art methods on the benchmark set. 5.      Apply the string kernel algorithms on lncRNA classification and evaluate the performance. 6.      If necessary, improve the string kernel algorithms to achieve better performance.Write a report to summarize the results.
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Computational Bioscience Research Center
Novel Pincer complexes for catalysis
Academic Program: Chemistry
The student will work on the design and synthesis of 2-amino-pyridine based pincer ligands (3-10 steps) and the preparation of their corresponding transition metal complexes. Students will trained on standard organic synthesis knowledge and Schlenk skills.  For more information see:https://pubs.acs.org/doi/10.1021/acscatal.8b04495​
BAS/1/1334-01-01
​Catalysis and Organometallics​
​Preparation and characterization of new complexes. Development of catalytic applications​ 
Chemistry
Physical Sciences and Engineering
KAUST Catalysis Center
Role of non-classical hydrogen bonding in organocatalysis
Academic Program: Chemistry
The student will utilize kinetic (NMR, IR, etc) and computational tools (DFT calculations) to elucidate the role of hydrogen bonding network and in particular the non-classical hydrogen bonding in the thiourea and guanidine-based organocatalysis.  ​​​​​
BAS/1/1334-01-01
​Chemistry
​Assist in the kinetic study and DFT calculations.​ 
Chemistry
Physical Sciences and Engineering
KAUST Catalysis Center
Deep Learning for Visual Computing
Academic Program: Computer Science
The internship will be in the area of visual computing (computer graphics, computer vision, remote sensing). The exact topic depends on the student's interest, student's background, and current research topics in thegroup. To give some examplesof past projects, our group worked on topics related to generative adversarial networks for synthesizing images, textures, point clouds, 3D geometry, ..., networks using graphs as representations, and 3D reconstruction problems such as depth from one or multiple images, primitive fitting, indoor room layout reconstruction and segmentation of images.​
BAS/1/1630-01-01
​Computer Graphics, Computer Vision, Deep Learning
​There are two learning objectives for the internships:1) students should learn about machine learning, deep learning, andtherespective target application chosen for the internship.​2) students should implement a working prototype
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Computer Graphics, Computer Vision, and Visualization
Academic Program: Computer Science
The internship is in the area of graphics, vision, or visualization. The exact topic is determined in discussion with the student to obtain a good fit with the student’s interest and background. Example projects are 3d reconstruction from images and laser scans, geo-spatial visualization, remeshing, sampling, procedural modeling, and design computation using machine learning. ​​​​​​
11111.0000000000
​Computer Science
​The student should either contribute to an existing research project or leading his/her own project. That includes reading literature, solving technical problems, and implementation.​
Computer Science
Computer, Electrical and Mathematical Sciences and Engineering
Visual Computing Center
Monitoring subsidence due to groundwater pumping in central Arabia
Academic Program: Earth Science and Engineering
Excessive pumping and use of groundwater aquifers in the Middle East is a seriousproblem, leading to aquifer depletion, permanent compaction and shortage of usablewater. One effective way of monitoring the aquifer usage is satellite radar mapping of cm-levelsubsidence due to groundwater pressure decrease in the aquifer systems. In thisproject, we plan to use such satellite radar observations of areas in central Arabia to mapout the extent and magnitude of ongoing subsidence. The results will provide an overviewof the problem of groundwater overuse and may help the authorities to take the necessaryactions.​​​​
BAS/1/1353-01-01
​Earth Science, Environmental science and engineering
The student(s) will learn how to process satellite radar data using Linux/shell based program routines. Theywill also need to learn how to work with the outcome (e.g. in Matlab), display the results, and to providequantitative assessment of the level of subsidence and extent of the groundwater over use problem. Theresults should be summarized in a short report at the end of the internship.
Earth Science and Engineering
Physical Sciences and Engineering