PhD position in Computational Biology (100%) The Department of Biomedical Engineering is part of the Faculty of Medicine at the University of Basel. It contributes to a better future in meeting health care needs through innovative biomedical research and engineering solutions, translating basic science into medical knowledge and healthcare innovations. The Pediatric Disease Modeling Lab (https://dbe.unibas.ch/en/research/data-driven-modelling-analysis/pediatric-disease-modeling-lab) offers a PhD research opportunity in the context of systems biology and mathematical modelling. Our mission is to understand how early-life exposures to the microbiome shape lifelong health through their impact on the developing immune system. We develop mathematical models of microbiome–immune co-development to quantify how early-life perturbations shape infectious disease dynamics, vaccine responses, and non-communicable diseases, with the aim of translating our insights into actionable strategies for pediatric care. Our work combines mechanistic mathematical modeling, causal inference, and machine learning approaches, with the opportunity to be applied to longitudinal multi-omics data from pediatric cohorts spanning diverse socio-economic and geographical contexts. Your position We are seeking a highly motivated PhD student to join our interdisciplinary research team. You will have the opportunity to apply cutting-edge quantitative methods and modeling approaches to diverse datasets with longitudinal multi-omics data from both local and international collaborations. You will be part of a collaborative research environment with close interactions with the Basel Research Centre for Child Health (BRCCH), the University Children's Hospital Basel (UKBB), and the Swiss Tropical and Public Health Institute (Swiss TPH). A strong interest in applying mathematical modelling to biological questions and excellent teamwork and communication skills in English are required. The PhD candidate will be expected to take an active role in shaping the project within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https://www.unibas.ch/en/Research/Values-Ethics/Diversity-and-Inclusion.html), we are committed to sustain and promote an inclusive culture, ensure equal opportunities and value diversity and respect in our working and learning environment. Depending on your interests and background, your main tasks will include: Analyzing and integrating longitudinal multi-omics data from pediatric cohorts Developing and parameterizing mechanistic mathematical models Applying statistical modeling, causal inference, and machine learning approaches Collaborating with experimental and clinical research partners Support and preparation of scientific reports and journal articles Your profile Essential: Master's degree in Computational Biology, Bioinformatics, Applied Mathematics, Physics, Statistics, Computer Science, Engineering, or a related quantitative field Strong interest in applying quantitative methods to biomedical questions Experience with programming (e.g., Python, R, or MATLAB) Excellent written and spoken English communication skills Motivation to work in an international research environment Desirable: Experience with statistical / mechanistic modelling or machine learning Familiarity with biological data analysis (e.g., microbiome, transcriptomics, or immunological data) Background in dynamical systems, Bayesian inference, or causal inference Interest in global health, immunology, or developmental biology We offer you A stimulating, interdisciplinary research environment at the intersection of computational science and pediatric health Access to unique longitudinal datasets from international pediatric cohorts Close collaboration with clinical partners and an international network of researchers Strong mentorship with opportunities for career development and scientific independence Participation in the PhD program in Biomedical Engineering at the University of Basel A dynamic and supportive team culture that values diversity and inclusion Key References B. Tepekule, A.I. Lim, and C.J.E. Metcalf, "The ontogeny of immune tolerance: a model of early-life secretory IgA - gut microbiome interactions", PLoS Biology, 2025. Link: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003263 B. Tepekule, J. Bergadà-Pijuan, T. Scheier, H. F. Günthard, M. Hilthy, R. D. Kouyos, S. Brugger, "Computational and in vitro evaluation of probiotic treatments for nasal Staphylococcus aureus decolonization", Proceedings of the National Academy of Sciences (PNAS), 2025. Link: https://www.pnas.org/doi/10.1073/pnas.2412742122 B. Tepekule, P. Abel Zur Wiesch, R. Kouyos, S. Bonhoeffer, "Quantifying the impact of treatment history on plasmid-mediated resistance evolution in human gut microbiota", Proceedings of the National Academy of Sciences (PNAS), 2019. Link: https://www.pnas.org/content/116/46/23106 Application / Contact The position is available from August 1, 2026, or later by mutual agreement. Review of applications will begin immediately and continue until the positions are filled. Please submit your application via the University of Basel Recruiting-Portal, by submitting the following documents: (A) a cover letter describing your research interests and motivation, (B) a complete curriculum vitae, (C) contact details of at least two references willing to provide recommendation letters upon request. For informal inquiries about the position, please contact Prof. Dr. Burcu Tepekule (burcu.tepekule@unibas.ch). The University of Basel is an equal opportunity and family-friendly employer committed to excellence through diversity. Apply www.unibas.ch Wenn Sie sich auf diese Stelle bewerben, erwähnen Sie bitte dass Sie sie auf Universitoxy gesehen haben.

PhD position in Computational Biology (100%)

The Department of Biomedical Engineering is part of the Faculty of Medicine at the University of Basel. It contributes to a better future in meeting health care needs through innovative biomedical research and engineering solutions, translating basic science into medical knowledge and healthcare innovations.
The Pediatric Disease Modeling Lab (https://dbe.unibas.ch/en/research/data-driven-modelling-analysis/pediatric-disease-modeling-lab) offers a PhD research opportunity in the context of systems biology and mathematical modelling. Our mission is to understand how early-life exposures to the microbiome shape lifelong health through their impact on the developing immune system. We develop mathematical models of microbiome–immune co-development to quantify how early-life perturbations shape infectious disease dynamics, vaccine responses, and non-communicable diseases, with the aim of translating our insights into actionable strategies for pediatric care. Our work combines mechanistic mathematical modeling, causal inference, and machine learning approaches, with the opportunity to be applied to longitudinal multi-omics data from pediatric cohorts spanning diverse socio-economic and geographical contexts.

Your position

We are seeking a highly motivated PhD student to join our interdisciplinary research team. You will have the opportunity to apply cutting-edge quantitative methods and modeling approaches to diverse datasets with longitudinal multi-omics data from both local and international collaborations. You will be part of a collaborative research environment with close interactions with the Basel Research Centre for Child Health (BRCCH), the University Children's Hospital Basel (UKBB), and the Swiss Tropical and Public Health Institute (Swiss TPH).
A strong interest in applying mathematical modelling to biological questions and excellent teamwork and communication skills in English are required. The PhD candidate will be expected to take an active role in shaping the project within an environment that encourages academic freedom and scientific independence.
In line with our and Uni Basel values (https://www.unibas.ch/en/Research/Values-Ethics/Diversity-and-Inclusion.html), we are committed to sustain and promote an inclusive culture, ensure equal opportunities and value diversity and respect in our working and learning environment.

Depending on your interests and background, your main tasks will include:

Analyzing and integrating longitudinal multi-omics data from pediatric cohorts
Developing and parameterizing mechanistic mathematical models
Applying statistical modeling, causal inference, and machine learning approaches
Collaborating with experimental and clinical research partners
Support and preparation of scientific reports and journal articles

Your profile

Essential:

Master's degree in Computational Biology, Bioinformatics, Applied Mathematics, Physics, Statistics, Computer Science, Engineering, or a related quantitative field
Strong interest in applying quantitative methods to biomedical questions
Experience with programming (e.g., Python, R, or MATLAB)
Excellent written and spoken English communication skills
Motivation to work in an international research environment

Desirable:

Experience with statistical / mechanistic modelling or machine learning
Familiarity with biological data analysis (e.g., microbiome, transcriptomics, or immunological data)
Background in dynamical systems, Bayesian inference, or causal inference
Interest in global health, immunology, or developmental biology

We offer you

A stimulating, interdisciplinary research environment at the intersection of computational science and pediatric health

Access to unique longitudinal datasets from international pediatric cohorts
Close collaboration with clinical partners and an international network of researchers
Strong mentorship with opportunities for career development and scientific independence
Participation in the PhD program in Biomedical Engineering at the University of Basel
A dynamic and supportive team culture that values diversity and inclusion

Key References
B. Tepekule, A.I. Lim, and C.J.E. Metcalf, "The ontogeny of immune tolerance: a model of early-life secretory IgA - gut microbiome interactions", PLoS Biology, 2025.
Link: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003263

B. Tepekule, J. Bergadà-Pijuan, T. Scheier, H. F. Günthard, M. Hilthy, R. D. Kouyos, S. Brugger, "Computational and in vitro evaluation of probiotic treatments for nasal Staphylococcus aureus decolonization", Proceedings of the National Academy of Sciences (PNAS), 2025.
Link: https://www.pnas.org/doi/10.1073/pnas.2412742122

B. Tepekule, P. Abel Zur Wiesch, R. Kouyos, S. Bonhoeffer, "Quantifying the impact of treatment history on plasmid-mediated resistance evolution in human gut microbiota", Proceedings of the National Academy of Sciences (PNAS), 2019.
Link: https://www.pnas.org/content/116/46/23106

Application / Contact

The position is available from August 1, 2026, or later by mutual agreement. Review of applications will begin immediately and continue until the positions are filled. Please submit your application via the University of Basel Recruiting-Portal, by submitting the following documents: (A) a cover letter describing your research interests and motivation, (B) a complete curriculum vitae, (C) contact details of at least two references willing to provide recommendation letters upon request.
For informal inquiries about the position, please contact Prof. Dr. Burcu Tepekule (burcu.tepekule@unibas.ch).

The University of Basel is an equal opportunity and family-friendly employer committed to excellence through diversity.

Apply

www.unibas.ch

Wenn Sie sich auf diese Stelle bewerben, erwähnen Sie bitte dass Sie sie auf Universitoxy gesehen haben.

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