- Degree
- MSc in Medical Engineering, specialisations: "Medical Image and Data Processing", "Health and Medical Data Analytics & Entrepreneurship" or "Medical Robotics"
- Course location
- Erlangen
- In cooperation with
- Medical Valley of the Metropolitan Region Erlangen-Nürnberg, University Hospital Erlangen, Siemens Healthineers, Fraunhofer Institute, Max Planck Institute and others
- Teaching language
- • English
- Languages
- It is possible to complete the whole Master's programme (including the Master's thesis) in English. Students with additional German skills can benefit from choosing from an even broader range of course options, if desired.
- Full-time / part-time
- • full-time
- Programme duration
- 4 semesters
- Beginning
- Winter and summer semester
- Application deadline
- Please refer to our programme website for current deadlines: https://www.medical-engineering.study.fau.eu/prospective-students/joining-the-master-degree-program/application/ (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.
- Tuition fees per semester in EUR
- None
- Combined Master's degree / PhD programme
- No
- Joint degree / double degree programme
- No
- Description/content
- Medical technology is one of the main research foci at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Over 150 university institutes and laboratories are actively participating in this sector. Many research units at FAU, especially at the Faculties of Engineering, Science, Medicine, and Economics deal with research questions in healthcare engineering, such as medical imaging and image processing (CT, PET, SPECT, US, OCT), molecular and translational medicine, computational medicine, medical and biological computer sciences, medical physics engineering, medical biotechnologies and biomaterials, robotic systems for medical applications, medical processes, and health management. An overview of our research highlights can be found on our medtech map: https://www.medtech.fau.eu/ (https://www.medtech.fau.eu/).
The Master's degree in "Medical Engineering – Specialisation: Medical Image and Data Processing" is a research-driven programme with a specialised focus on computer science for medical needs. It teaches systematic and instrumental methods to students and imparts professional and methodological skills. The interdisciplinary study content leads to a high qualification in the career field of medical image and data processing. Methods for software systems in medical engineering are acquired, ranging from algorithms for image improvement, image reconstruction, and image registration to computer-aided diagnostic support and hospital information systems.
The Master's degree in "Medical Engineering – Specialisation: Health and Medical Data Analytics & Entrepreneurship" is an interdisciplinary, research-driven programme, which offers a profound education in data science for medical applications and hands-on entrepreneurship training. The latter is addressed from an innovative, user-centred perspective, enabling students to identify market demands and respond to them by using top-notch technical skills. The core curriculum consists of competencies in data science (acquisition, storing, pre-processing, modelling). Moreover, students receive a state-of-the-art education in innovation and entrepreneurship, allowing them to identify opportunities and execute a project from the business perspective (value proposition canvas, stakeholder management, business plan, etc.).
The Master's degree in "Medical Engineering – Specialisation: Medical Robotics" provides research-focused education at the intersection of computer science / artificial intelligence and electrical engineering. It teaches students in-depth knowledge in the analysis and development of robotic systems for applications in the fields of medical treatment, rehabilitation and patient care. The topics include fundamentals of robotics, computational neurotechnology and human-machine interfaces.
For all branches of study, very advanced computer science skills (algorithms, programming in Java, C/C++, Matlab, Python, working with Linux operating system), profound knowledge in electrical engineering, and a strong theoretical background in mathematics are required.