Overview The programme trains specialists who can apply advanced artificial intelligence to biomedical problems — from improving diagnostics and drug discovery to enabling personalised treatments and more efficient healthcare delivery. It brings together computer science, engineering and medical perspectives so graduates develop both state-of-the-art AI skills and the domain knowledge needed to deploy those methods responsibly in clinical and biotechnological settings.
Structure and aims This is a research-oriented, two-year Master's degree offered in English at the Technical University of Munich in cooperation with Friedrich-Alexander‑Universität Erlangen‑Nürnberg, and it includes an optional Research Excellence Certificate. The curriculum is designed to prepare students for careers in academic research and high-impact industrial innovation by emphasising independent scientific thinking, methodological rigour and the ability to push forward AI technologies in biomedicine.
Subject focus Coursework and research span the full AI landscape — machine learning, data science and related fields — with special attention to areas critical for healthcare adoption: Trustworthy AI that clinicians, patients and regulators can accept; Human-centred AI to support personalised care and effective human–AI collaboration; and Multi‑modal and Generative AI for learning from complex biomedical data and producing meaningful, novel outputs.
Key facts and admission checklist
This two-year, full‑time master’s curriculum (120 credits) is organized across four thematic areas and delivered over two semesters each academic year. Core coursework builds technical depth, while applied modules and a supervised master’s thesis—carried out at TUM, FAU, or with academic/industry partners—give students hands‑on experience in biomedical AI. The structure is designed to balance rigorous foundations with practical implementation, interdisciplinary collaboration and professional development.
Key modules include Foundations of AI, which focus on the algorithmic and mathematical underpinnings of modern AI; Applications of AI in Biomedicine and Healthcare, which present real‑world AI implementations in medical and health settings; and a Focus Subject, where you choose a specialisation to gain deeper expertise in either theoretical foundations or applied topics. Cross‑Cutting Themes teach transferable skills such as entrepreneurship and innovation, science communication, and public and patient engagement. The programme culminates in an independent research project (the master’s thesis) that integrates taught material with original research or applied development.
An optional Research Excellence Certificate (REC) adds at least 30 credits to the standard curriculum for students who want a stronger research profile. REC candidates take an extra cross‑cutting course, a second focus subject with an additional elective, an extra seminar and an additional research project. Graduates who complete the full 150‑credit path (120 + 30) receive the Master of Science in AI in Biomedicine with the distinction “with Honours,” signifying enhanced research training and achievement.
Requirements and key facts
The selection is carried out through a two-stage aptitude assessment intended to judge how well applicants match the specific demands of the Master's programme. The process focuses on applicants' academic and practical preparedness for advanced study in AI applied to biomedical contexts.
Applicants should have a solid foundation in mathematics, machine learning and programming. You must already hold, or be on track to complete, one of the following qualifications (or an equivalent degree):
Winter Semester (International)
15 March 2026
Winter Semester (EU/EEA)
31 May 2026
Graduates are prepared for careers in academic research as well as high‑impact roles in industry. Typical paths include PhD studies and research positions in universities, research institutes or corporate R&D teams, and technical roles in biotech, pharmaceutical companies, medical device manufacturers, clinical AI groups and health‑tech startups.
The programme’s combination of technical depth and biomedical domain knowledge also suits positions such as machine learning engineer, data scientist for healthcare, clinical AI specialist, or product and research roles that bridge engineering and clinical practice. English instruction and international collaborations help prepare students for the German and broader European job markets.
Technical University of Munich — München
Technical University of Munich — München
Hochschule Fresenius - University of Applied Sciences — Berlin
Dresden International University — Dresden