Research Scientist, Virtual Cell Modelling & Perturbative Biology Foundation Models

Research Scientist role building multimodal foundation models to predict cellular responses to chemical and genetic perturbations, using generative AI and machine learning to accelerate drug discovery.

• Research and develop generative and distributional models (e.g., flow matching, diffusion models) to predict high-dimensional cellular responses.
• Build and maintain ML systems capable of processing massive multiomics datasets on high-performance compute clusters.
• Work closely with colleagues to ensure model predictions are interpretable, trustworthy, actionable, and grounded in real experimental outcomes.
• Design and implement rigorous evaluation metrics that test generalization across cellular context, unseen perturbations and covariates.
• Publish findings in top-tier venues (e.g., NeurIPS, ICML, Nature, Science, Cell) and contribute to the broader scientific community.

• PhD (or equivalent) with significant academic or industry research experience in machine learning applied to drug discovery, life sciences or other real-world scientific or engineering problems.
• Strong background in generative modeling and representation learning, with experience applying these to high-dimensional scientific data.