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Lead research in developing machine learning methods for organoid phenotyping and high-throughput screening, collaborating with experimental scientists to advance computational approaches in drug discovery. Ideal for candidates with a strong research background in computational biology, computer science, or machine learning.
Tasks
- Lead a research project developing computational methods to address challenges in organoid engineering and drug discovery.
- Develop state-of-the-art statistical and machine learning methods to tackle questions in human biology and disease.
- Work with high-content datasets from complex human model systems, such as large-scale perturbation experiments with multi-modal readouts.
- Collaborate with experimental scientists at the Institute of Human Biology (IHB) to shape future experiments and develop methods with translational impact.
- 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 and contribute open-source tools to the broader scientific community.
- Gain exposure to drug discovery and development while building scientific independence as a computational researcher.
Requirements
- 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.
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Über uns
Roche ist ein international tätiges Schweizer Pharma- und Diagnostikunternehmen mit Sitz in Basel und entwickelt, produziert und vertreibt Medikamente sowie diagnostische Lösungen. Das Unternehmen ist in den Bereichen Pharma und Diagnostik tätig und fokussiert sich auf Forschung und Innovation in verschiedenen Therapiegebieten wie Onkologie, Neurologie und Infektionskrankheiten. Roche ist weltweit aktiv und zählt zu den führenden Unternehmen im Gesundheitsbereich.
Das Team
The ML for Biosystems Engineering group at the Institute of Human Biology (IHB) in Basel, Switzerland, is a highly collaborative environment with outstanding computational and experimental scientists.