Overview This research-oriented Master's at FAU builds on the university’s substantial medtech expertise—over 150 institutes and laboratories across the Faculties of Engineering, Science, Medicine and Economics contribute to the field. FAU’s research portfolio spans medical imaging modalities (CT, PET, SPECT, ultrasound, OCT), molecular and translational medicine, computational medicine, medical and biological computer science, medical physics, biotechnologies and biomaterials, robotic systems for healthcare applications, and health management. See an overview of FAU’s medtech activities here: https://www.medtech.fau.eu/.
Specialisations and focus Students choose one of three specialisations. Medical Image and Data Processing emphasizes computer-science methods for medical applications, training you in algorithmic and software approaches such as image enhancement, reconstruction, registration, computer-aided diagnostic support and hospital information systems. Health and Medical Data Analytics & Entrepreneurship combines in-depth data-science training (data acquisition, storage, preprocessing and modelling) with practical entrepreneurship education—user-centred innovation, market analysis and business planning (e.g., value proposition, stakeholder management). Medical Robotics sits at the intersection of computer science/AI and electrical engineering and covers robotic fundamentals, computational neurotechnology and human–machine interfaces for treatment, rehabilitation and patient care.
Who this suits The programme is interdisciplinary and research-driven, preparing students for advanced technical roles or further research in healthcare engineering, medical imaging, data analytics or robotic systems. Coursework and projects focus on developing professional and methodological skills grounded in computation, engineering and applied medical questions, leveraging FAU’s broad medtech research network.
Entry/academic requirements (concise)
This Master's programme combines advanced technical coursework with practical lab work, seminars and soft-skills training, and is offered with three specialisations: Medical Image and Data Processing; Health and Medical Data Analytics & Entrepreneurship; and Medical Robotics. Students choose from a range of compulsory and elective courses within each module group to tailor the programme to their interests and career goals. Teaching formats include lectures, laboratory courses and seminars, giving a balance of theoretical grounding and hands‑on experience.
A distinct research laboratory module is embedded in the curriculum and is conducted in a Faculty of Engineering laboratory. This module develops professional research capabilities, including experimental techniques, data handling and scientific writing. Soft skills (for example language courses) are also part of the curriculum to strengthen communication and intercultural competence. The programme culminates in a 30‑credit Master’s thesis that is supervised by both a technical and a medical advisor, emphasising interdisciplinary collaboration between engineering and clinical practice.
Graduates leave with advanced technical knowledge in their chosen specialisation, practical laboratory experience, and the ability to conduct and present scientific research. The structure supports career paths in medical device development, clinical research, medical data science, and entrepreneurial activities in healthcare technology, while the flexible course choices let students focus on either deeper engineering expertise or applied medical innovation.
Key curriculum components and credit requirements
Total programme credit load: 120 credits. Course formats include lectures, lab courses and seminars; students may select specific course propositions within the compulsory module groups to match their professional interests.
This master’s programme requires a strong technical foundation and specific subject knowledge. Applicants must hold a relevant Bachelor's degree and meet a minimum grade threshold based on the German grading scale (1.0 best — 4.0 passing). Candidates from non-technical undergraduate backgrounds (for example medicine, biochemistry, nursing) are not eligible. International applicants should check how their national grades translate to the German system when preparing their application.
In addition to the degree and grade requirement, successful applicants need solid prior knowledge in mathematics, computer science, and electrical engineering, practical experience with Linux, and advanced programming ability. Each application is reviewed individually by the programme’s entrance qualification board; suitable applicants will be invited to a 60-minute online admission exam that assesses mathematics, algorithms, and foundational topics relevant to the chosen specialisation (electrical engineering or computer science/artificial intelligence). A self-assessment tool is available on the programme website to help you gauge your readiness before applying: https://www.medical-engineering.study.fau.eu/prospective-students/joining-the-master-degree-program/self-assessment/
Admission requirements (bullet points)
Winter Semester (International)
Please refer to our programme website for current deadlines:https://www.medical-engineering.study.fau.eu/prospective-students/joining-the-master-degree-program/application/.We strongly advise non-EU applicants to apply as early as possible in order to prevent visa problems.
Graduates are prepared for technical and research roles in the medical-technology sector, including positions in medical imaging and diagnostic software development, medical data science and analytics, clinical IT systems, and the design or integration of medical robotic systems. The combination of deep technical training, an embedded research lab and industry connections supports entry into research groups, development teams at MedTech companies, hospitals and clinical research centres.
Those who complete the Health & Medical Data Analytics & Entrepreneurship track gain additional competencies to pursue innovation-driven careers: roles in product management, start-ups, technology transfer, or positions that bridge engineering and business development within the healthcare industry. The programme’s collaborations with regional industry and research organisations can help candidates access internships, project partnerships and employer networks.
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