The Paediatric Disease Modeling Group Seeks a PhD Student and a Postdoctoral Researcher
visual: University of Basel, Mark Niedermann
PhD Position
The group is seeking a highly motivated PhD student to join their 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.
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
Learn more and apply here
Postdoctoral Fellow
The group is seeking a highly motivated postdoctoral researcher to join their interdisciplinary team. You will develop and apply advanced statistical and causal inference methods to characterize developmental trajectories from longitudinal multi-omics data, with the goal of understanding how early-life perturbations shape long-term health outcomes across diverse pediatric populations.
You will have the opportunity to work with rich datasets from international pediatric cohorts spanning diverse geographic and socioeconomic contexts, including 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.
Depending on your interests and background, your work may involve:
- Developing methods to characterize developmental patterns from multi-omics data
- Developing and parameterizing mechanistic mathematical models describing microbiome-immune dynamics
- Applying Bayesian inference and model fitting approaches
- Applying statistical modeling, causal inference, and machine learning approaches to identify determinants of developmental robustness
- Applying causal inference approaches to identify critical windows in development
- Optimizing intervention strategies through computational experimentation
- Collaborating with experimental and clinical research partners
- Contributing to scientific publications and grant applications
Learn more and apply here
The Paediatric Disease Modeling Lab at the University of Basel offers a PhD Research Opportunity and a Post-Doctoral Research Opportunity in the context of systems biology and mathematical modelling. The group's mission is to understand how early-life exposures to the microbiome shape lifelong health through their impact on the developing immune system. They 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 their insights into actionable strategies for pediatric care. Their 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.