Technische Universität Dresden Invites Applications for Postdoctoral Research Fellow in Multi-Omics and Machine Learning
Pharm'Up 
The research group of Prof. Dr. Nikolaos Perakakis at the Technische Universität Dresden (TU Dresden) has announced a prestigious full-time Postdoctoral Research Fellow position. Operating within a vibrant scientific ecosystem, the position focuses on leveraging advanced machine learning and multi-omics data integration to unravel the mechanisms, diagnostics, and treatments of metabolic diseases such as obesity, diabetes, and metabolic-dysfunction associated steatotic liver diseases.
Research Framework and Collaborative Network
The position is deeply embedded in TU Dresden’s prominent scientific community and collaborates closely with leading national and international initiatives. Key partner networks include:
- The Paul Langerhans Institute Dresden (PLID): A core center dedicated to metabolic research.
- The German Center for Diabetes Research (DZD): A nation-wide partnership focusing on translational diabetes research.
- The TransCampus Initiative: A strategic biomedical research partnership with King’s College London.
Fellows will gain direct access to large, well-characterized clinical cohorts and experimental models to drive high-impact translational research.
Core Responsibilities
- Conducting multi-omics data integration (proteomics, metabolomics, genomics, and transcriptomics) and advanced statistical analyses for ongoing and newly initiated projects.
- Developing and applying cutting-edge machine learning models aimed at biomarker discovery, patient stratification, and the prediction of disease trajectories.
- Collaborating seamlessly in an interdisciplinary environment alongside clinicians, bioinformaticians, systems biologists, and experimental researchers.
- Authoring high-impact scientific publications, presenting findings at international conferences, and actively contributing to the guidance and supervision of Ph.D. students and junior researchers.
Candidate Profile and Eligibility Requirements
- Academic Background: A Ph.D. in Bioinformatics, Computational Biology, Systems Biology, Biostatistics, or a closely related field, acquired no earlier than 2021 (maximum 6 years from the doctoral degree acquisition).
- Technical Expertise: Demonstrated hands-on experience in analyzing multi-omics datasets alongside strong expertise in machine learning and advanced statistical modeling.
- Programming Skills: High proficiency in Python and/or R, with practical familiarity using omics and machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or Bioconductor.
- Desirable Traits: Prior exposure to clinical or biomedical datasets is highly valued, while background knowledge in immunology and FACS analysis is considered an added advantage.
- Communication: Excellent written and spoken English communication skills paired with a strong publication record.
Fellowship Terms and Application Process
- Duration and Compensation: This is a full-time position funded for up to 3 years (minimum 2-year commitment), with remuneration based on the German TV-L public service salary scale alongside standard benefits.
- Preferred Start Date: November 1, 2026, or at the earliest mutually convenient date thereafter.
- Required Application Materials: Candidates must consolidate their application into a single PDF file comprising a cover letter (maximum 1 page), a comprehensive curriculum vitae with a full publication list, and the contact details of at least two professional referees.
- Submission Details: Applications should be sent directly via email to Prof. Dr. Nikolaos Perakakis at
Nikolaos.Perakakis@ukdd.de. Applications will be evaluated on a rolling basis until the position is successfully filled.
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