Medical Engineering – Specialisations: Medical Image and Data Processing | Health and Medical Data Analytics & Entrepreneurship | Medical Robotics
FAU Erlangen-Nürnberg
Degree
Master of Science
Language
English
Tuition
Free
Duration
4 semesters
Programme Overview
Research-driven English-taught master's programme offering three specializations: Medical Image and Data Processing (computer science for medical imaging), Health and Medical Data Analytics & Entrepreneurship (data science with innovation training), or Medical Robotics (AI and robotics for medical applications).
Admission Requirements
- Academic requirements
Online admission exam in mathematics, algorithms, and basic knowledge in chosen branch of study
- Minimum GPA
- No fixed minimum GPA is published on the current official programme page.
- English requirements
- Whole programme including Master's thesis can be completed in English.
- German requirements
- German skills not required for admission but highly recommended for daily life
- Accepted bachelor fields
- Bachelor's degree in computer science, electrical engineering, or related field with very advanced computer science skills (algorithms, programming in Java, C/C++, Matlab, Python, Linux), profound electrical engineering knowledge, and strong mathematics background.
- APS Certificate
- Yes
- Thesis required
- Yes
- Mandatory internship
- No
Costs & Funding
- Tuition fee
- None
- Semester contribution
- Approximately 140 EUR per semester
- ECTS credits
- 120
- Funding info
Tuition: None Semester contribution: Approximately 140 EUR per semester
Career Outcomes
Graduates work in medical technology sector including medical imaging, data analytics for healthcare, medical robotics, health IT, and medical device companies with expertise in computational medicine and medical systems engineering.
Specialisations
Typical job roles
Target industries
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Programme details
- Degree
- Master of Science
- Language
- English
- Duration
- 4 semesters
- Mode
- full-time
- Start
- Winter Semester
- Deadline
- Rolling (annual)
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