Eindhoven University of Technology

What you'll study

This master's focuses on the intersection of artificial intelligence, systems engineering and embedded technology. Teaching combines core AI methods with systems-level thinking so you learn to develop robust, deployable intelligent systems rather than only theoretical models.

• Core topics: machine learning and statistical inference, optimisation, probabilistic modelling, deep learning, knowledge representation and reasoning.
• Systems and engineering: control theory, real-time and embedded systems, sensor fusion, perception (computer vision, signal processing), and hardware–software co-design for AI.
• Integration and validation: algorithm engineering, model interpretability and verification, safety and reliability of AI systems, simulation and digital twins, and testing in realistic environments.
• Methodology and ethics: data engineering and pipelines, experimental design, reproducibility, and responsible AI including privacy, fairness and societal impact.
• Project work and thesis: hands-on team projects with industry partners, lab assignments using robotics and embedded platforms, and an independent research or industrial thesis project supervised by TU/e faculty.

Students typically choose elective courses to tailor the degree toward areas such as autonomous robotics, industrial AI, medical and health AI systems, or edge/embedded AI. The programme is project-heavy, emphasises practical implementation, and culminates in a substantial master thesis that can be research- or industry-based.

Entry requirements

Applicants are expected to hold a relevant bachelor's degree in computer science, electrical engineering, mechanical engineering, mathematics or a closely related technical discipline. Demonstrable background in programming, linear algebra, probability and statistics, and basic control or signal processing is required.

• Academic transcript: a recognised university degree with a solid quantitative component.
• Prerequisite knowledge: coursework or experience in programming (Python, C/C++ or equivalent), calculus and linear algebra, probability and basic algorithms and data structures. Applicants lacking some prerequisites may be offered conditional admission with bridging courses.
• Supporting documents: CV, motivation statement outlining fit and goals, and academic references. For some candidates, a portfolio of projects or coding samples will strengthen the application.
• English proficiency: evidence of proficiency in English through a recognised qualification or prior instruction in English at degree level where required by the university.

Career prospects

Graduates are prepared for technical and leadership roles where AI must be integrated into engineered products and services. Typical career paths include:

• AI systems engineer or machine learning engineer building production-ready AI components for robotics, autonomous vehicles, industrial automation or consumer products.
• Robotics engineer or perception engineer working on computer vision, sensor fusion and control for mobile and collaborative robots.
• Embedded/edge AI developer delivering optimized models for constrained hardware in IoT and smart devices.
• Data scientist or applied researcher in industry R&D teams, developing and validating algorithms for real-world deployment.
• Systems architect or technical consultant specialising in AI-enabled systems integration for manufacturing, healthcare or mobility sectors.
• Continued academic research: graduates may also progress to PhD programmes to pursue research in AI foundations, trustworthy AI or cyber-physical systems.

Why study at Eindhoven University of Technology

Eindhoven University of Technology (TU/e) is a technology-focused university with strong interdisciplinary research and education in engineering and computer science. The university’s close ties with the Brainport ecosystem and well-established technology companies provide frequent collaboration opportunities, industry projects and internship pathways. Teaching emphasises hands-on experimentation in modern labs, access to robotic platforms and embedded systems facilities, and supervision by researchers active in applied AI, control and systems engineering.

Students benefit from a compact, engineering-driven campus culture where coursework is designed to combine rigorous theory with practical engineering practice, preparing graduates to deploy AI in demanding, safety-critical and industrial contexts.