Post Doctorate Scientist

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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 to develop machine learning methods for organoid phenotyping and high-throughput screening. Join us to develop next-generation computational methods for drug discovery with complex human model systems.

You will lead a research project developing computational methods to address important challenges in organoid engineering and drug discovery. Working in a highly collaborative environment with outstanding computational and experimental scientists, you'll develop state-of-the-art statistical and machine learning methods to tackle challenging questions in human biology and disease.

You’ll have the chance to work with rich, high-content datasets from complex human model systems, such as large-scale perturbation experiments with multi-modal readouts. In collaboration with experimental scientists at IHB, you will also have the opportunity to shape future experiments, enabling you to develop methods with direct translational impact.

• Causal inference and mechanism discovery from perturbation experiments
• Predictive modeling and analysis for high-throughput screening
• Computational methods for quantitative comparison of spatial, genomic, and other molecular profiles from organoid models with patient data

Your work will connect computational innovation with experimental applications, contributing to the development of next-generation human model systems for drug discovery. You will publish your research, contribute open-source tools to the broader scientific community, and gain exposure to drug discovery and development while building your scientific independence as a computational researcher.

• 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 Tensor Flow
• 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

• Experience applying machine learning to biomedical data, including genomics, imaging, spatial, or multimodal datasets
• Experience with perturbation experiments, causal inference, high-throughput screening, or predictive modeling
• Contributions to open-source software or scientific computing tools
• Experience working closely with experimental collaborators

The Institute of Human Biology (IHB) is a research center in Basel, Switzerland,…