71 Short courses in Computer Science & IT in Saudi Arabia

The Cyber Security and Resilience of LEO Satellite Constellations (Internship) project from King Abdullah University of Science and Technology (KAUST) covers the paradigm shift from classical GEO satellites (e.g., EUROSAT) to LEO satellites (e.g., Starlink). 

The Federated Learning (Internship) project at King Abdullah University of Science and Technology (KAUST) 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. 

The Pollux - Autoregressive Video Generation as a World Model (Internship) project from King Abdullah University of Science and Technology (KAUST) aims to build a video generation model with three key advancements.

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. 

This Scaling Graph Neural Networks to 1000s of GPUs (Internship) project from King Abdullah University of Science and Technology (KAUST) 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.

The Foundations of Private and Fair Statistics (Internship) project at King Abdullah University of Science and Technology (KAUST) will explore basic statistical model or problems for different types of data in the differential privacy or fairness model.

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 Cyber Secure and Resilient System-on-Chip using FPGA Dynamic Configuration (Internship) project from King Abdullah University of Science and Technology (KAUST) covers the genuine immutability properties of hardware components and elements make them ideal for security hardening and containment, i.e., by making the hard-implemented logic tamper-resistant against both benign and intrusion faults.

The Accelerated Simulation of Reactive Flows Using Deep Neural Networks (Internship) project from King Abdullah University of Science and Technology (KAUST) aims to develop an algorithm for accelerated computations by developing high fidelity reduced-order data-based chemical kinetics solver using autoencoder and neural network algorithm. 

The 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.