Postdoctoral Researcher - Machine Learning for Organoid Phenotyping

About the role

The Institute of Human Biology (IHB) in Basel, Switzerland, part of Roche's ML for Biosystems Engineering group, is seeking a Postdoctoral Researcher to develop machine learning methods for organoid phenotyping and high-throughput screening. The project leads to next-generation computational methods for drug discovery with complex human model systems.

Key responsibilities

• Lead a research project developing computational methods for organoid engineering and drug discovery
• Develop statistical and machine learning methods with computational and experimental scientists
• Work with high-content datasets from complex human model systems, such as large-scale perturbation experiments with multi-modal readouts
• Shape future experiments with experimental scientists at IHB to give methods direct translational impact
• Publish research, contribute open-source tools and build scientific independence as a computational researcher

Required profile

• PhD in computational biology, computer science, machine learning, bioinformatics or a related technical field
• Track record of independent research shown through publications, preprints or substantial research software contributions
• Strong research experience in statistical or machine learning methods, ideally on genomics or other high-dimensional biological data
• Ability and motivation to work closely with experimental scientists in interdisciplinary projects

Required skills

• Proficiency in Python and modern ML frameworks such as JAX, PyTorch or TensorFlow
• Scientific programming and software engineering practices, including version control, testing and reproducible research workflows
• Nice to have: experience with biomedical data (genomics, imaging, spatial, multimodal), perturbation experiments, causal inference, high-throughput screening, open-source contributions and close collaboration with experimental collaborators

To apply, submit a CV (including a list of relevant publications) and a cover letter describing your research interests.