University Medical Center Hamburg-Eppendorf
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PhD Position in Computational Biology and Deep Learning for Spatial Omics University Medical Center Hamburg-Eppendorf in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD position in a newly funded BMFTR junior research consortium. The position is 36 months at 65% of regular weekly working hours, with dedicated resources for travel and conferences and structured doctoral training through UKE graduate programmes.
Deadline
Oct 6, 2026
Country
Germany
University
University Medical Center Hamburg-Eppendorf

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About this position
PhD position at the University Medical Center Hamburg-Eppendorf (UKE) in the Institute of Medical Systems Bioinformatics for a project in computational biology, deep learning, and spatial omics. The project focuses on integrating spatial transcriptomics, digital histopathology, and clinical data to study the molecular basis of glomerular kidney diseases.
The position is part of a newly funded BMFTR junior research consortium (“Zukunft eHealth”) involving computational scientists, pathologists, and nephrologists at UKE Hamburg and RWTH Aachen University Hospital, with a partner in Rome. The PhD student will be based in Hamburg and will have regular joint meetings with the partner group in Aachen.
Research directions include either spatial omics integration and analysis or geometric/topological deep learning for tissue structure, with methods released as open-source software and evaluated on a well-characterised cohort of human kidney biopsies. The work is highly interdisciplinary and clinically oriented, with opportunities to present at international conferences and publish in peer-reviewed journals.
Funding: fully funded PhD position, fixed-term for 36 months, at 65% of regular weekly working hours. The post mentions dedicated resources for travel and conferences, structured doctoral training, and access to modern GPU infrastructure and clinical expertise.
Eligibility: applicants should hold a master's degree or equivalent in bioinformatics, computer science, computational biology, physics, mathematics, statistics, or a related quantitative field. Strong Python programming skills and experience with scientific Python tools are required; experience with PyTorch or similar deep learning frameworks is also required. A solid background in statistics and machine learning is expected. Experience with single-cell or spatial transcriptomics or computational pathology is helpful but not mandatory. Good English is required; German is not required.
Application deadline: 2026-10-06. Apply with a cover letter, CV, academic transcripts, and contact details for one or two references. Links to code repositories or a thesis are welcome. Applicants should also indicate whether they prefer the spatial omics integration project or the geometric/topological deep learning project.
Funding details
Fully funded PhD position in a newly funded BMFTR junior research consortium. The position is 36 months at 65% of regular weekly working hours, with dedicated resources for travel and conferences and structured doctoral training through UKE graduate programmes.
What's required
Master's degree or equivalent in bioinformatics, computer science, computational biology, physics, mathematics, statistics, or a related quantitative field; strong programming skills in Python and the scientific Python ecosystem; experience with deep learning frameworks such as PyTorch or similar; solid foundation in statistics and machine learning; interest in geometric or topological deep learning, graph neural networks, or related methods; experience with single-cell or spatial transcriptomics data or computational pathology is an advantage but not required; interest in biomedical questions; willingness to work closely with clinicians and experimental scientists; good written and spoken English; German is not required; independent, careful, and collaborative working style.
How to apply
Prepare a cover letter, CV, academic transcripts, and contact details for one or two references. You may also include links to code repositories or a thesis, and indicate whether you prefer the spatial omics integration or geometric/topological deep learning project. Submit via the UKE application portal.
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