Delft University of Technology

What you'll study

This programme emphasises applied artificial intelligence: core machine learning and probabilistic modelling, practical deep learning, scalable systems and the engineering practices needed to take models into production. Teaching balances formal foundations with project-based learning, group design sprints and an extended capstone in collaboration with industry or research groups.

• Foundations of AI and Machine Learning — supervised and unsupervised learning, optimisation, feature engineering, model evaluation and uncertainty estimation.
• Deep Learning and Representation Learning — neural network architectures, transfer learning, sequence and graph models, and practical training techniques.
• Probabilistic Modelling & Bayesian Methods — probabilistic inference, graphical models and methods for modelling uncertainty in decision-making systems.
• Human–AI Interaction and Responsible AI — interpretability, fairness, privacy-preserving methods, ethics and the social impacts of deployed systems.
• MLOps and Production AI — model lifecycle, deployment pipelines, monitoring, reproducibility, software engineering practices and cloud/edge considerations.
• Systems for Large-scale AI — distributed training, data engineering, performance engineering and considerations for real-time/low-latency systems.
• Domain Applications and Electives — applied modules or projects in areas such as robotics, healthcare, energy, mobility or industrial systems, enabling cross-disciplinary specialisation.
• Capstone Project / Thesis — an extended, supervised project carried out individually or in a small team, typically undertaken with an industrial partner or research group to solve a concrete applied AI problem.

Entry requirements

Applicants are expected to hold a recognised bachelor’s degree in computer science, electrical engineering, data science, mathematics, or a closely related quantitative subject, demonstrating strong competence in programming and mathematical foundations (linear algebra, probability and calculus). Practical experience with programming (Python or similar) and exposure to basic machine learning concepts are usually required. Selection will also consider academic transcripts, letters of recommendation and a statement of purpose outlining motivation and relevant experience. Proof of English proficiency is required for applicants whose prior degree was not taught in English.

Career prospects

Graduates are prepared for technical and product-facing roles that require both algorithmic understanding and engineering ability. Typical career paths include:

• Applied Machine Learning Engineer / AI Engineer — building and deploying models in production systems.
• Data Scientist — designing data-driven solutions, advanced analytics and experimentation.
• AI Systems Engineer / MLOps Engineer — specialising in model lifecycle, deployment pipelines and scalable infrastructure.
• AI Product Manager or Consultant — translating business needs into technical AI solutions and guiding cross-functional delivery.
• Research or PhD programmes — for graduates aiming to pursue further research in machine learning, robotics or related areas.

Why study at Delft University of Technology

Delft University of Technology combines a strong engineering tradition with active research in AI, robotics and data science. The university has close ties with industry and national research institutes, enabling collaborative capstone projects and internships with technology companies and public-sector partners. Teaching emphasises hands-on, project-driven learning and interdisciplinary collaboration, supported by well-equipped labs and access to high-performance computing resources. Students benefit from a campus environment that fosters entrepreneurship and links to the broader Dutch tech ecosystem, providing pathways into both start-ups and established organisations working on applied AI challenges.