PhD Programme in Biomedical Data Science
Interdisciplinary PhD training at the intersection of biomedical research and data science in Hannover, Germany.
Best for:Best for applicants with a strong background in computer science, bioinformatics, or health sciences who are eager to bridge disciplinary gaps and contribute to cutting-edge biomedical data science research.
Overview
The PhD Programme in Biomedical Data Science (BIOMEDAS) at Hannover Medical School (MHH) integrates computer science, life sciences, and health-related disciplines to address the growing challenge of processing complex biomedical data. Funded through the Translationsallianz in Niedersachsen (TRAIN), this programme trains students to combine disciplinary expertise with data science skills, focusing on AI, machine learning, and biomedical modelling. Supervised collaboratively by partners including Helmholtz Centre for Infection Research and Leibniz University Hanover, students engage in interdisciplinary research projects spanning personalised medicine, clinical data exploitation, and AI-driven diagnostics. The programme offers structured training, international collaboration, and integrated language courses in English and German.
What you'll do
- Conduct independent research at the intersection of biomedical data science and health sciences, guided by a collaborative thesis advisory committee.
- Develop and apply advanced data science techniques, including machine learning, statistical modelling, and computational methods, to biomedical datasets.
- Collaborate with interdisciplinary research partners across institutions to advance joint projects in personalised medicine, diagnostics, and clinical data analysis.
- Participate in structured training programmes, including core research projects, seminars, and interdisciplinary workshops.
- Present research findings at national and international conferences and publish results in peer-reviewed journals.
- Engage in integrated language courses to enhance proficiency in English and German for academic and professional purposes.
Requirements
Degree required: A successfully completed university degree in computer science-related fields (e.g., medical computer science, bioinformatics, physics, engineering, computer science, statistics, mathematics) OR in health science-related fields (e.g., medicine, veterinary medicine, pharmacy, life sciences). Applicants from computer science backgrounds must demonstrate proficiency in biomedical research, while applicants from health sciences must show competence in data science.
Degree offered: PhD
Language: English proficiency at B2 level (no formal proof required). German language skills are not mandatory but integrated courses are provided.
Instruction: English (with integrated German language courses)
- Proven expertise in either computer science or health sciences with demonstrated competence in the complementary field (e.g., biomedical research for computer scientists or data science for health scientists).
- Strong analytical and programming skills, particularly in data mining, machine learning, or statistical modelling.
- Ability to work collaboratively in interdisciplinary teams.
- Familiarity with biomedical data formats, databases, and clinical data standards.
- No nationality restrictions.
- Applicants must hold a relevant master’s degree or equivalent in computer science, health sciences, or related fields.
- No age restrictions.
- No prior experience caps, but interdisciplinary collaboration skills are essential.
Funding & terms
Programme and admissions
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- Tuition
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How to apply
Applicants must submit their application via email to **biomedas@translationsallianz.de**. The application should include proof of degree, evidence of interdisciplinary competence (e.g., coursework, projects, or publications), and a statement of research interest. No formal deadline is listed, but applications are processed on a rolling basis.
- CV
- Proof of completed university degree (certified copy)
- Transcripts of academic records
- Statement of research interest (1-2 pages)
- Proof of interdisciplinary competence (e.g., publications, projects, or coursework)
- Letter of motivation
Contact
BIOMEDAS Programme Office· Programme Coordinator
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