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Postdoctoral Research Fellow (m/f/d)

Anticipated Start Date:  01.11.2025
Application End Date:  24.08.2025
Temporary Employment:  36 months
Working Hours:  Full and Part Time
Requisition ID:  1452

The University Hospital Carl Gustav Carus and the Faculty of Medicine together form University Medicine Dresden. It is committed to excellence in high-performance medicine, medical research and teaching as well as healthcare services for patients in the entire region.

Together for the cutting-edge medicine of tomorrow - become part of  University Medicine Dresden

 

Postdoctoral Research Fellow (m/f/d)

at the Department and Outpatient Clinic III in the field of Metabolic Vascular Medicine

 

 

 

The position is to be filled starting November 1, 2025, either full-time or part-time, and is initially limited to 3 years. Remuneration will be based on the classification regulations of the Collective Agreement for the Public Service of the German Federal States (TV-L) and, if the personal requirements are met, assignment to pay group E13 TV-L is possible.

 

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.

 

 

Your responsibilities:

 

  • 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

 

 

Your Profile:

 

  • PhD in Bioinformatics, Computational Biology, Systems Biology, Biostatistics, or a related field – (Doctoral degree should not have been acquired earlier than 2020)
  • 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 (e.g., scikit-learn, TensorFlow/PyTorch, Bioconductor)
  • Experience with clinical and/or biomedical data is highly desirable
  • Experience in the field of immunology and of FACS analysis is a plus
  • Strong publication record in relevant fields
  • Ability to work independently and in a collaborative, interdisciplinary environment
  • Excellent written and spoken English communication skills

 

Our Offer:

 

  • An inspiring and collaborative research environment at Technische Universität Dresden, the Paul Langerhans Institute Dresden (PLID) – part of the German Center for Diabetes Research (DZD) – as well as the TransCampus initiative

  • Access to extensive, well-characterized clinical cohorts and experimental models

  • Opportunities to contribute to cutting-edge translational research with direct benefits for patients with metabolic diseases

  • Offers for professional development, mentoring, and scientific networking

  • Remuneration: according to TV-L, including a fixed annual special payment and a company pension scheme

  • Vacation: 30 days of paid leave; Christmas Eve and New Year’s Eve are also days off if they fall on a weekday

  • Work-life balance: our family office provides advice and support for all life situations, including assistance with daycare places near the hospital, holiday programs, or care for relatives

  • Health: extensive sports and fitness programs in our state-of-the-art gym, as well as mental health programs and counseling, workplace health management, and much more

  • Employee benefits: staff discounts at our hospital pharmacy, corporate benefits, and other shopping portals

  • Mobility: subsidy for the job ticket and the possibility of a parking space in our parking garage or on the hospital grounds

 

Your contact in the HR Directorate

 

 

Elena Koch

Tel: 0351-458- 2594

Severely disabled applicants are expressly encouraged to apply and will be given preferential consideration if equally qualified.

We kindly ask you to apply preferably via our online form to make the selection process faster and more effective. Of course, we also consider your written application without any disadvantages. 

To ensure the best possible health protection for our patients and employees, we require proof of measles vaccination (a medical certificate) in accordance with the Infection Protection Act. Further proof of vaccination status must be presented before starting work.

Good reasons for the Dresden University Medicine as an employer

Attractive conditions

With us, you can expect remuneration in line with the company pay scale, 30 days' vacation and a fixed annual bonus. For the time after your active career, we offer the possibility of a company-supported pension scheme.

Diverse working environment

Dresden University Hospital is one of the most innovative and successful hospitals in Germany. As a maximum care provider, it covers the entire spectrum of medicine and offers varied tasks in a wide range of areas.

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