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Dominik Heider

Professor at University of Münster

University of Münster

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Germany

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Research Interests

Cell Biology

20%

Statistics

10%

Machine Learning

20%

Genetic

20%

Medical Science

20%

Molecular Biology

20%

Bioinformatic

20%

Positions2

Publisher
source

Dominik Heider

University Name
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University of Münster

PhD Student (Bioinformatician) – Machine Learning for Biomedical Data

This fully funded PhD position is offered at the Institute of Medical Informatics, University of Münster, within the bioinformatics service unit of the research group “Machine Learning for Biomedical Data” led by Professor Dominik Heider. The position is embedded in the DFG-funded Collaborative Research Centre 1748, Principles of Reproduction, which brings together scientists from the University, University Hospital, Max Planck Institute Münster, and RWTH Aachen. The central research objective is to elucidate the genetic, molecular, and cellular mechanisms governing testis formation and function, sperm production and function, fertilisation, and early embryonic development in both health and disease. The successful candidate will join a vibrant interdisciplinary team and contribute to cutting-edge research at the intersection of molecular biology, cell biology, physiology, biophysics, genetics, and informatics. The role involves developing, implementing, and maintaining bioinformatics pipelines for large-scale omics data, including RNASeq and single-cell sequencing (scSeq). You will also contribute to the development of new algorithms and methods for efficient analysis of omics datasets, participate in workflow automation and management using systems such as Snakemake, and engage in the Integrated Research Training Group ‘Reproduction.MS PhD-Training Centre in Translational Science’. The position offers a competitive salary according to the TV-L E13 tariff agreement, extra annual payment, company pension plan (VBL), 30 days of vacation per year plus two additional days off, and access to state-of-the-art computing infrastructure. The research environment is highly interdisciplinary and international, with a strong track record of collaboration. The structured PhD training programme provides a wide range of professional development opportunities, and the university offers additional benefits such as sports programs, job ticket, company events, cafeteria, and emergency childcare. Applicants should hold a Master’s degree in Computer Science, Bioinformatics, Medical Informatics, Computational Biology, or a related discipline. Required skills include knowledge of algorithm development and bioinformatics pipelines, initial experience in biological data analysis (e.g., omics technologies), and programming skills in Python, R, or C/C++. Familiarity with workflow management systems such as Snakemake is beneficial. Candidates should be highly motivated for scientific work, willing to contribute to an interdisciplinary team, and possess excellent English communication skills; German language skills are advantageous but not required. To apply, submit your application via the University of Münster career portal by February 11, 2026. Your application should include a letter of motivation (max 2 pages), a summary of your Master’s thesis (max 1 page), transcript and scanned copies of your degree certificates, and two letters of recommendation or contact details of two references. For further inquiries, contact Professor Dominik Heider at [email protected].

just-published

Publisher
source

Dominik Heider

University Name
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University of Münster

PhD Student in Machine Learning for Biomedical Data – CRC 1748 Principles of Reproduction

The University of Münster is offering a fully funded PhD position in the research group 'Machine Learning for Biomedical Data' led by Professor Dominik Heider at the Institute of Medical Informatics. This position is part of the DFG-funded Collaborative Research Centre 1748, 'Principles of Reproduction', which brings together scientists from the University, University Hospital, Max Planck Institute Münster, and RWTH Aachen. The CRC 1748 aims to elucidate the genetic, molecular, and cellular mechanisms underlying testis formation, sperm production and function, fertilisation, and early embryonic development in both health and disease. The successful candidate will join a dynamic, interdisciplinary team focused on developing innovative machine learning methods for biomedical data analysis. Research topics include explainability, interpretability, causal inference, and the design of novel algorithms for large-scale omics datasets. The position also involves multi-modal machine learning model development and data integration, contributing to high-impact biomedical research with clinical relevance. As part of the Integrated Research Training Group ‘Reproduction.MS PhD-Training Centre in Translational Science’, the PhD student will benefit from a structured training programme, state-of-the-art computing infrastructure, and a collaborative, international research environment. The position is full-time (100%) and fixed-term for three years, with a competitive salary according to the TV-L E13 tariff agreement, extra annual payment, and company pension plan (VBL). Applicants should hold a Master’s degree in Computer Science, Data Science, Medical Informatics, or a related discipline. Essential skills include knowledge of machine learning, statistics, algorithm development, and programming experience (Python, R, C/C++). Experience with biological data analysis (e.g., omics technologies) is desirable. Candidates must demonstrate high motivation for scientific work, willingness to work in an interdisciplinary team, and excellent English communication skills; German language skills are advantageous but not required. To apply, submit your application via the University of Münster career portal by February 11, 2026. Required documents include a letter of motivation (max 2 pages), a summary of your Master’s thesis (max 1 page), transcripts and scanned copies of degree certificates, and two letters of recommendation or contact details of two references. For further information, contact Prof. Dominik Heider at [email protected].

just-published