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784 Short courses in Computer Sciences

Physics Summer School
The Physics Summer School course at Imperial is led by Professor David Colling and Dr Alexander Richards from Imperial’s Department of Physics.

Learn Python with Generative AI
Accelerate your Python skills with generative AI—gain hands-on experience, real-time feedback, and industry-aligned techniques with the Johns Hopkins Engineering's Learn Python with Generative AI

Introduction to Ethical Hacking
Johns Hopkins Engineering's Introduction to Ethical Hacking certificate program teaches you how to uncover vulnerabilities, threats, and potential risks before attackers do—and how to protect systems with confidence.

IT (Web Design)
Want to enter a fast-paced, growing profession that lets you showcase your technical and creative skills? Take the next step in your career with the IT (Web Design) course from International Career Institute (ICI). These flexible learning programmes will equip you with the skills you need to thrive in this dynamic industry.

Continual Learning (Internship)
The goal of the Continual Learning (Internship) project from King Abdullah University of Science and Technology (KAUST) is to develop and improve the capability of the machine learning methods not to forget older concepts as time passes.

Channel-Adaptive Machine Learning-Based mmWave Beamforming (Internship)
The Channel-Adaptive Machine Learning-Based mmWave Beamforming (Internship) project from King Abdullah University of Science and Technology (KAUST) focuses on integrating machine learning algorithms into mmWave beamforming to dynamically adapt to changing channel conditions.

Internet Of Things - An Introduction
This course provides a practical, interdisciplinary introduction to the Internet of Things and to the broader area of Pervasive Computing. University of Derby offers the Internet Of Things - An Introduction programme.

Dexterous Robot Manipulation using Physics Engine (Internship)
The Dexterous Robot Manipulation using Physics Engine (Internship) project from King Abdullah University of Science and Technology (KAUST) explores the idea of building computational models based on physics engines and design feedback algorithms to autotune parameters of the model whenever the robot detects the discrepancy between the simulation and its experience in the physical world.

Real-Time Power Grid Monitoring using PMU-Based Synchrophasor Communication (Internship)
This Real-Time Power Grid Monitoring using PMU-Based Synchrophasor Communication (Internship) project from King Abdullah University of Science and Technology (KAUST) focuses on the configuration, emulation, and testing of Phasor Measurement Units (PMUs) within a Real-Time Digital Simulator (RTDS).

AI Engineer Using Microsoft Azure Nanodegree
Join this AI Engineer Using Microsoft Azure Nanodegree programme offered by Udacity, built in collaboration with Microsoft.

Back-End Development
Become a back-end developer with advanced programming skills in JavaScript, cloud services, and .NET. Learn to use AI for efficient development and quality assurance, and prepare for a career as a full-stack developer with the Back-End Development degree at Noroff School of Technology and Digital Media.

Towards a Principled Understanding of Deep Learning (Internship)
The Towards a Principled Understanding of Deep Learning (Internship) project is offered at King Abdullah University of Science and Technology (KAUST).

LLM Injection Cyber Resilient Assistants (Internship)
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.

Numerical Approximation of Partial Differential Equations (Internship)
This Numerical Approximation of Partial Differential Equations (Internship) project from King Abdullah University of Science and Technology (KAUST) will involve the study and analysis of the properties of the approximation of some partial differential equations.

Dynamic Malware Analysis using LLMs (Internship)
The Dynamic Malware Analysis using LLMs (Internship) project from King Abdullah University of Science and Technology (KAUST) covers the increasing complexity of malware highlights the need for advanced analysis tools, both static and dynamic, for effective reverse engineering and behavioral analysis of a given sample.

Inverse Problems in Imaging (Internship)
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.

Classification of long non-coding RNAs (Internship)
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.

Professional Certificate of Competency in IEC 61850 Based Substation Automation
This Professional Certificate of Competency in IEC 61850 Based Substation Automation from the Engineering Institute of Technology is designed for engineers and technicians who need to understand the techniques required to use and apply IEC 61850 to substation automation, hydropower plants, wind turbines, and distributed energy resources as productively and economically as possible.

Trustworthy Autonomous Vehicles Architecture (Internship)
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.

Federated Learning (Internship)
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.