A research group at Technische Universität Dresden is hiring a Postdoctoral Research Fellow (m/f/d) in Metabolic-Vascular Medicine. You will combine multi-omics data and machine learning to understand, diagnose and treat metabolic diseases.
About the Research
Topics: Obesity, diabetes and metabolic-dysfunction associated steatotic liver disease
Data: Proteomics, metabolomics, genomics and transcriptomics
Method: Machine learning on large clinical cohorts and experimental models
Network: German Center for Diabetes Research (DZD) and TransCampus with King’s College London
Benefits & Career Growth
Pay: TV-L, pay grade E13 possible, plus annual bonus
Leave: 30 days paid vacation
Training: Wide internal and external development options
Mobility: Job ticket subsidy
Health: Fitness, mental health programs and a Family Office, with flexible work options
Position Overview
This postdoc role is built around multi-omics integration and modeling. You will develop machine learning models for biomarker discovery, patient grouping and disease trajectories, and publish the results.
Why This Role Matters: You join a vibrant research community with national and international partners, work across clinical, computational and experimental fields, and can build a strong publication record.
Key Responsibilities
Data & Modeling
- Carry out multi-omics data integration and advanced statistical analyses in current and new metabolic disease projects
- Build and apply machine learning models for biomarker discovery, patient stratification and disease trajectory prediction
Research & Teamwork
- Work with clinicians, bioinformaticians, systems biologists and experimental researchers
- Publish high-impact papers and present at national and international conferences
- Help supervise PhD students and junior researchers
Qualifications & Requirements
Qualification
- PhD in bioinformatics, computational biology, systems biology, biostatistics or a related field, awarded in 2021 or later (maximum 6 years since the doctorate)
Experience
- Proven multi-omics analysis experience (proteomics, metabolomics, genomics or transcriptomics)
- Strong machine learning and statistical modeling skills
- Python and/or R programming with machine learning and omics libraries
- Strong publication record
Essential Skills
- Independent and collaborative working style in an interdisciplinary setting
- Excellent written and spoken English
- Desirable: clinical or epidemiological data experience, plus immunology and FACS analysis
About TU Dresden & Postdoc Careers in Saxony
The position runs for 24 months from 1 December 2026, full or part time. A full application includes a one-page cover letter, a CV with publication list and contact details of at least two references. The employer details in the benefits suggest a link to the university hospital. Contact details are left out here.
Career Excellence: Use machine learning to find new biomarkers in metabolic disease research at TU Dresden.
 Who Should Apply?
- Bioinformaticians: With a recent PhD
- Computational Biologists: Working with omics data
- Biostatisticians: Interested in metabolic disease
- Machine Learning Researchers: Seeking biomedical impact
- Early-Career Scientists: Building a publication record
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