Research Scientist, Virtual Cell Modelling & Perturbative Biology Foundation Models

This role focuses on building multimodal foundation models to predict cellular responses to chemical and genetic perturbations using generative and distributional modeling. The research aims to replace wet-lab experiments with in silico predictions for drug discovery. The position involves developing scalable ML systems, designing evaluation frameworks, and collaborating with a multidisciplinary team of ML researchers and computational biologists.

• 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, and grounded in real experimental outcomes
• Design and implement rigorous evaluation metrics that test generalization across cellular contexts, unseen perturbations, and covariates
• Publish findings in top-tier venues (e.g., NeurIPS, ICML, Nature, Science, Cell)

• 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