Postdoctoral Research Fellow (m/f/d) - #2747415

Postdoctoral Research Fellow (m/f/d)

in the field of Metabolic-Vascular Medicine

The position is available starting December 1, 2026, on a full-time or part-time basis for a fixed term of 24 months. Remuneration is based on the provisions of the Collective Agreement for the Public Service of the Federal States (TV-L); classification in pay group E13 is possible, subject to the fulfillment of personal requirements.

The research group of Prof. Dr. Nikolaos Perakakis is seeking for a highly motivated and talented Postdoctoral Research Fellow (max. 6 years from the acquisition of the doctoral degree) to join our interdisciplinary team at the Technische Universität Dresden, focusing on the integration of multi-omics data to better understand, diagnose, and treat metabolic diseases (i.e. obesity, diabetes, metabolic-dysfunction associated steatotic liver diseases).

Our group is part of a vibrant scientific community at the TU Dresden and works closely with national and international partners within initiatives such as the German Center for Diabetes Research (DZD) and the TransCampus collaboration with King's College London. We analyze large clinical cohorts and experimental models to unravel disease mechanisms and identify novel biomarkers by integrating proteomics, metabolomics, and genomics / transcriptomics data with machine learning techniques.

• Conduct multi-omics data integration and advanced statistical analyses in ongoing and newly initiated projects on metabolic diseases
• Develop and apply machine learning models for biomarker discovery, patient stratification, and prediction of disease trajectories
• Collaborate with clinicians, bioinformaticians, systems biologists, and experimental researchers in a highly interdisciplinary setting
• Publish high-impact scientific papers and present results at national and international conferences
• Contribute to the supervision of PhD students and junior researchers

• PhD in Bioinformatics, Computational Biology, Systems Biology, Biostatistics, or a related field – (Doctoral degree should not have been acquired earlier than 2021)
• Proven experience in the analysis of multi-omics data (proteomics, metabolomics, and/or genomics, transcriptomics)
• Strong expertise in machine learning and advanced statistical modeling
• Proficient programming skills in Python and/or R, experience with machine learning and omics analysis libraries
• Experience with clinical and/or biomedical data and/or epidemiologic studies is highly desirable
• Strong publication record in relevant fields
• Experience in the field of immunology and of FACS analysis is a plus
• Ability to work independently and in a collaborative, interdisciplinary environment
• Excellent written and spoken English communication skills

• Compensation: Based on the TV-L collective bargaining agreement in pay grade E13, plus an annual bonus, provided you meet the personal requirements
• Vacation: 30 days of paid vacation
• Professional Development: Extensive internal and external training and development opportunities to continuously strengthen your skills
• Company Culture: We value transparency, team spirit, and shared success. Celebrate with us at company gatherings or sporting events like the Rewe Team Challenge
• Mobility: A subsidy for a job ticket to make your commute eco-friendly and convenient
• Health: We promote your well-being with modern fitness and exercise programs at our health center, mental health programs, and a comprehensive workplace health management program
• Shopping benefits: Take advantage of employee discounts at our clinic pharmacy, exclusive corporate benefits, and other shopping portals
• Work-life balance: Our Family Office supports you with questions regarding daycare spots, vacation programs, and caring for family members. We also offer flexible work arrangements

A complete application package includes a cover letter (maximum 1 page), a curriculum vitae (including a list of publications), and contact details for at least two references. For preliminary information, please contact Prof. Dr. Nikolaos Perakakis via email at [email protected]