60 Short courses in Computer Sciences in Saudi Arabia

The Inverse Problems in Imaging (Internship) project from King Abdullah University of Science and Technology (KAUST) aims to teach students about inverse problems, and critical techniques for solving them, including convex and non-convex optimization, sparse coding, and compressive sensing.​

The goal of this Imagination Inspired Vision (Internship) project at King Abdullah University of Science and Technology (KAUST) is to focus on developing techniques that empower AI machines to see the world (computer vision) or to create novel products (e.g., fashion and art), hence the name Imagination Inspired Vision.

The Classification of long non-coding RNAs (Internship) project from King Abdullah University of Science and Technology (KAUST) is to apply and further improve the string kernel algorithms developed in Prof. Gao’s group to the lncRNA classification problem. ​​​​​​​

The aim of this Improving Solar Corona Simulations with Advanced Boundary Conditions (Internship) project from King Abdullah University of Science and Technology (KAUST) is to advance the accuracy of solar corona simulations by developing a predictive model for short-term (ideally hourly) solar farside magnetograms.

This Deep Learning and Machine Intelligence for Single Cell Genomics (Internship) project from King Abdullah University of Science and Technology (KAUST) covers subjects such as single cell biology and genomics in particular are currently transforming the biosciences.

The aim of this Non-terrestrial Communication System for Aerial-based Object Recognition and Positioning (Internship) from King Abdullah University of Science and Technology (KAUST) is to utilize civilian single-lens drones to develop a cost-effective system for detecting and tracking objects from high-altitude perspectives and establish an energy-efficient non-terrestrial communication system. 

The Trustworthy Autonomous Vehicles Architecture (Internship) project from King Abdullah University of Science and Technology (KAUST) addresses is the failure of existing AV architectures to reconcile AI/ML-based stochastic decision-making with the deterministic, time-sensitive nature of driving control. 

The Transaction Graph Analysis in the Cardano Network (Internship) project from King Abdullah University of Science and Technology (KAUST) focuses on analyzing the transaction graph of the Cardano blockchain to uncover structural and behavioral insights into the network.

The Next Generation 3d Understanding (Internship) project at King Abdullah University of Science and Technology (KAUST) covers 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.

The Study on Optimal Hybrid Beamforming Architecture for 6G Systems (Internship) project from King Abdullah University of Science and Technology (KAUST) covers subjects such as hybrid beamforming is crucial for future 6G networks, offering a balance between hardware complexity and energy efficiency.

This Nonlinear Partial Differential Models (Internship) project from King Abdullah University of Science and Technology (KAUST) students 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.​​​​​

The Algorithmic Information Theory for Machine Intelligence (Internship) project from King Abdullah University of Science and Technology (KAUST) is perfect for those seeking hands-on exposure to research or co-op experience, and many interns find it excellent preparation for future graduate studies.

This Learning to Cooperate in Multi-robot Task Allocation (Internship) project from King Abdullah University of Science and Technology (KAUST) aims to implement these models on a multi-robot platform and validate the effectiveness of the models through lab experiments.

Modern supervised machine learning models are trained using enormous amounts of data, and for this distributed computing systems are used. The Gradient Compression for Distributed Training of Machine Learning Models (Internship) project is offered at King Abdullah University of Science and Technology (KAUST).

This LLM Injection Cyber Resilient Assistants (Internship) project from King Abdullah University of Science and Technology (KAUST) develops LLM injection-resilient cyber assistants that combine safety and adaptability.

The Efficient Power Amplifier Design at 140 GHz (Internship) project from King Abdullah University of Science and Technology (KAUST) involves designing a high-efficiency power amplifier (PA) operating at 140 GHz for sub-THz communications using advanced SiGe technology. 

This Extension of the High-Order Entropy-stable Discontinuous Galerkin Solver to Non-Equilibrium Flows (Internship) project from King Abdullah University of Science and Technology (KAUST) teaches you about hypersonic flows encountered during atmospheric re-entry and high-speed aerospace operations demand robust CFD techniques.

This Mathematics of Generative AI for Protein Design (Internship) project from King Abdullah University of Science and Technology (KAUST) will build on existing tools (such as RFDiffusion) to generate nanobody (mini-antibody)  proteins, that recognize biomarkers or pathogens. 

This LLM-based Assistant for L-x Security Operation Center (SOC) Analyst (Internship) project from King Abdullah University of Science and Technology (KAUST) explores the feasibility and capabilities of using LLMs for detection and for different analysts' assistant levels.

This Learning Generative Causal Models from Sparse Temporal Observations during Cellular Reprogramming (Internship) project from King Abdullah University of Science and Technology (KAUST) covers how ​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.