Post Doctorate Scientist

Lead a research project developing computational methods to address important challenges in organoid engineering and drug discovery. Work collaboratively with computational and experimental scientists to develop state-of-the-art statistical and machine learning methods for questions in human biology and disease. Work with high-content datasets from complex human model systems, including large-scale perturbation experiments with multi-modal readouts. Collaborate with experimental scientists at IHB and help shape future experiments. Possible research areas include causal inference and mechanism discovery from perturbation experiments; predictive modeling and analysis for high-throughput screening; and computational methods for quantitative comparison of spatial, genomic, and other molecular profiles from organoid models with patient data. Connect computational innovation with experimental applications, contributing to next-generation human model systems for drug discovery. Publish research, contribute open-source tools to the scientific community, and build scientific independence as a computational researcher.

The ML for Biosystems Engineering group, led by Jonas Fleck at the Institute of Human Biology (IHB) in Basel, Switzerland, is seeking a Postdoctoral Researcher. The IHB is a research center dedicated to engineering advanced human model systems for drug discovery, development, and precision medicine. It fosters an interdisciplinary environment bridging academic and pharmaceutical research and connecting biologists, engineers, and data scientists. It has close ties to Roche's Pharmaceutical Research and Early Development (pRED) organization and Genentech (gRED), and collaborates with leading academic institutions including ETH Zürich, University of Basel, and EPFL, as well as globally. Basel is an international hub for research and innovation.

• PhD in computational biology, computer science, machine learning, bioinformatics, or related technical fields.

• Track record of conducting impactful independent research, demonstrated through publications, preprints, or substantial research software contributions.

• Strong research experience in statistical or machine learning methods, ideally applied to genomics or other high-dimensional biological data.

• Proficiency in Python and experience with modern ML and scientific computing frameworks such as JAX, PyTorch or TensorFlow.

• Strong scientific programming and software engineering practices, including version control, testing, reproducible research workflows, and agentic development tools.

• Creativity, scientific curiosity, and enthusiasm for developing computational methods that address challenging problems in biomedical research.

• Ability and motivation to work closely with experimental scientists and contribute to collaborative, interdisciplinary projects.

• Nice to have: Experience applying machine learning to biomedical data, including genomics, imaging, spatial, or multimodal datasets.

• Nice to have: Experience with perturbation experiments, causal inference, high-throughput screening, or predictive modeling.

• Nice to have: Contributions to open-source software or scientific computing tools.

• Nice to have: Experience working closely with experimental collaborators.

• To be considered, your application needs a CV (including a list of relevant publications) and a cover letter describing your research interests.