AI in Biomedicine – Elite Master's Program

Professional Profile: Data Science and Artificial Intelligence (PP DSAI)

Study Mode: Full-time

Language of Instruction: English

Duration of Studies: 4 Semesters

Credits: 120 ECTS + optional 30 ECTS for Research Excellence Certificate

Main Location: Garching

Program Start: Winter Semester

Costs: Semester Fees and Tuition Fees for Non-EU/EEA/Swiss

Information on scholarships and waivers for international students can be found here: Scholarships and Waivers

The AI in Biomedicine program is designed as a two-year full time Master's program (120 credits), with an optional Research Excellence Certificate (REC, additional 30 credits). Each academic year consists of two semesters.

The curriculum will be divided into four main topics: Foundations of AI courses offer knowledge about algorithmic and mathematical aspects of AI, while Applications of AI in Biomedicine and Healthcare courses introduce students to AI implementations in the medical and health sectors. Additionally, students will choose a Focus Subject to gain in-depth insights into foundations and/or applications of AI.  Finally, the Cross-Cutting Themes topic enables students to gain skills in entrepreneurship & innovation, science communication, public and patient engagement. The curriculum is rounded off by the Master's thesis at TUM or FAU, or at one of our academic or industry partners.

Research Excellence Certificate (REC): Students can earn at least 30 credits in addition to the curriculum to graduate with the REC. For this, the students will select an additional Cross-Cutting Themes course, as well as a second focus subject with an additional elective course, an additional seminar and an additional research project. Students completing the additional REC (120 credits+ 30 credits) will be awarded a Master’s degree (M.Sc.) in AI in Biomedicine with the special grade "with Honours".

More details on the Degree Program Contents

Graduates of AIBM are trained to pursue careers in cutting-edge research in both academia and industry, as well as roles in startups, policy-making, and global health innovation, making AIBM graduates key contributors to shaping the future of healthcare and technology.

TUM supports students throughout their lives in terms of job search, career entry, and career development. For further details, refer to the TUM Career Service.

The application for admission to the master’s degree program in AI in Biomedicine is to be completed and submitted using TUMonline Applicant portal:

How to apply for a Master’s Degree Program – Step by Step

Only in the case of admission, some additional documents must be submitted as certified copies for enrollment.

Prospective students with a Bachelor's degree from outside Germany have to request preliminary examination documentation from uni-assist (VPD) in advance.

Each study program at TUM sets its own entry requirements to ensure students have the skills and knowledge to successfully complete it. The application requirements described below are specific to AIBM:

1. Passport
2. Curriculum Vitae (CV)
3. Preliminary Documentation (VPD) issued by Uni-Assist (required for degrees from outside Germany)
4. APS – Certificate of the Academic Evaluation Center (required for degrees from Chinese, Vietnamese or Indian universities)
5. English Language Proficiency
6. Transcript of Records and Module Catalog
7. Curriculum analysis – Applicants should select the one below that applies to their degree and upload the generated PDF to their application in TUMonline:
   → For applicants with a background in Electrical and Computer Engineering (ECE)
   → For applicants with a background in Computer Science
   Important Note for All Applicants: Only the credits specified on the transcript should be entered; any modules still in progress should not be included. Applicants studying or who have graduated from a related program that does not match one of the two curricular analyses above should choose the form that best aligns with their degree and upload only one in TUMonline. We encourage applicants to take time to carefully consider the selection, as the submitted analysis will be used during the admission evaluation.
8. Statement of Purpose
9. Essay.
    

Further information on the required documents can be found in the Glossary of Documents.

Please carefully review the details provided in the International Applicant Guide. 

Preliminary documentation from uni-assist (VPD)

Applicants with an undergraduate degree from outside Germany must apply for a preliminary documentation (VPD) from uni-assist, in addition to the TUM application. Since this can take up to four weeks, we recommend initiating the process early.

Certificate from the German Evaluation Center (Akademischen Prüfstelle, APS)

Applicants with an undergraduate degree from China, India, or Vietnam must submit a certificate from the German Evaluation Center (APS).

Grade conversion

The TUM Grade Conversion Formula gives an initial indication of the GPA. Note that this tool provides only an estimate.

Tuition fees for students from non-EU countries

At the Technical University of Munich (TUM), tuition fees are charged for international students from third countries who newly enroll in a degree program starting from the winter semester of 2024/25.
 

We strongly recommend that students from non-EU countries apply early, preferably before March 31, to allow enough time for visa processing and finding accommodation.

The AIBM curriculum includes a total of 8 mandatory courses (41 credits) in the areas of Foundations of AI and Cross-cutting Themes:

The focus subject allows students to specialize in one of four domains:

• Advanced Machine Learning
• Drug Discovery and Computational Biology
• Imaging and Sensing
• Natural Language Processing and Speech

Each focus subject combines theoretical foundations acquired through specialized elective courses with hands-on, research-oriented training provided in the Master’s seminar and in a clinical applications project. An individual study plan is compiled for each student in close collaboration with the student's personal mentor (more information under “AIBM Mentoring”).

Clinical applications project in Cardiology/Neurology/Oncology

The students work on advanced clinical application projects in the area of the chosen focus subject under realistic conditions, often in collaboration with clinical or industrial partners.

Master's seminar

TUM and FAU offer a wide range of seminars, allowing students to choose from a broad spectrum of topics. Students must earn 5 credits from the following list. 

Different topics are offered for the modules in this list every semester. The seminar should be chosen within the area of the focus subject. 

Specialized elective courses

Students must earn at least 10 credits in their focus subject from this module list. The catalogue of elective courses is continuously updated by the examination board.

Modules from FAU can also be recognized, e.g. Deep Learning, Advanced Deep Learning, Pattern Recognition, Flat Panel CT, or Pattern Analysis. The examination board decides on the recognition.

Regardless of the chosen focus subject, students must earn 15 credits from elective modules in the area of Applications in Biomedicine and Healthcare and additional 4 credits in the area of Cross-cutting Themes:

As part of the six-month Master’s Thesis (30 credits), students are required to independently address a specific and complex research question within the field of AI in Biomedicine, applying the specialized knowledge and methodological skills acquired during their studies.

The Master's Thesis module typically serves as the final examination, consisting of two components: a scientific report (graded) and a presentation (ungraded). Students may be allowed to begin the thesis earlier if they have successfully completed the clinical applications project. The total duration, including the submission of the thesis, must not exceed six months from the start date.

The Master’s Thesis can be supervised by one of the participating chairs at TUM or FAU. Collaboration with external research institutions or industry partners, both domestic and international, is also possible and highly encouraged. 

Available theses at the TUM School of Computation, Information and Technology are published here.

Very ambitious students can earn at least 30 credits in addition to the AIBM curriculum to graduate with the research excellence certificate (Prädikat “with Honours”). For this, the students choose a second focus subject. Students need to:

• earn additional 6 credits from specialized elective courses in the area of the second focus subject
• finish a clinical applications project in cardiology/neurology/oncology in the area of the second focus subject
• earn additional 5 credits from a Master's seminar in the area of the second focus subject
• earn additional 4 credits from the cross-cutting themes area.

If the clinical applications project in the area of the first focus subject has already been completed at TUM or FAU, the additional one should preferably be completed either with one of the industrial or academic partners.

TUM - School of Computation, Information and Technology

• Prof. Dr. Zeynep Akata, Interpretable and Reliable Machine Learning
• Dr. Martin Menten, Artificial Intelligence in Healthcare and Medicine
• Prof. Dr. Nassir Navab, Computer Aided Medical Procedures
• Prof. Dr. Daniel Rückert, Artificial Intelligence in Healthcare and Medicine
• Prof. Dr. Julia A. Schnabel, Computational Imaging and AI in Medicine
• Prof. Dr. Dr. Fabian Theis, Mathematical Modelling of Biological Systems

TUM - Other Schools

• Prof. Dr. Martin Boeker, Medical Informatics (TUM School of Medicine and Health)
• Prof. Dr. Ruth Müller, Science & Technology Policy (TUM School of Social Sciences and Technology)
• Prof. Dr. Daniel Roth, Machine Intelligence in Orthopedics (TUM School of Medicine and Health)
• Prof. Dr. Peter Schüffler, Computational Pathology (TUM School of Medicine and Health & TUM School of Computation, Information and Technology)
• Prof. Dr. Björn Schuller, Health Informatics (TUM School of Medicine and Health & TUM School of Computation, Information and Technology)
• Prof. Dr. Christian Wachinger, Artificial Intelligence in Radiology (TUM School of Medicine and Health)

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

• Prof. Dr. med. Thomas Ganslandt, Medical Informatics, Biometry and Epidemiology (FAU Faculty of Medicine)
• Prof. Dr. Jana Hutter, Smart Imaging and Data Profiling (FAU Faculty of Medicine)
• Prof. Dr. Bernhard Kainz, Image Data Exploration and Analysis (FAU Faculty of Engineering)
• Prof. Dr. Andreas Maier, Pattern Recognition (FAU Faculty of Engineering)
• Prof. Dr. Vincent C. Müller, Theory and Ethics of Artificial Intelligence (FAU Faculty of Philosophy)
• Dr. Paula Andrea Pérez-Toro, Pattern Recognition (FAU Faculty of Engineering)

As of January 2026

AI in Biomedicine offers a 1:1 mentoring and coaching system, which pairs each student with a personal mentor. This allows the students to discuss electives of their curriculum, get feedback on their progress, and also discuss career choices. Furthermore, the students will be mentored in the development of essential professional skills, including entrepreneurship, innovation management, and science communication.

In addition, AIBM-associated faculty and visiting guests help students build their very first professional network and learn about career options and role models.

Being part of the AI in Biomedicine Elite Master's Program also offers great opportunities for the students' careers and professional network. As a member of the Elite Network of Bavaria, students will, for instance, receive exclusive invitations to seminars, workshops and events. The ENB Community enables them to connect with other elite students and initiate joint activities. We expect from our AIBM students to participate in this peer group and to contribute to this unique institution for outstanding talents.