Chercheur ou chercheuse scientifique, Modélisation de cellules virtuelles et modèles de fondation en biologie perturbative
Core
Building multimodal foundation models to predict cellular responses to chemical and genetic perturbations using petabytes of omics and imaging data to replace or augment wet-lab screening.
Role type
Senior scientific researcher (generative ML & representation learning)
Builds
Production ML models for in silico drug discovery
Domain
Biotechnology / Generative AI / Drug Discovery
Deliverable
production ML models
Required skills
Generative modeling (flow matching, diffusion), representation learning, high-performance computing, Python, scientific publication, cross-disciplinary collaboration
Preferred skills
Biology or chemistry background, compiled languages, wet-lab experimental paradigms
Technologies
Python, high-performance computing clusters
Responsibilities
Develop generative and distributional models for multidimensional cellular responses; Build and maintain scalable ML systems for massive multi-omics datasets; Collaborate with colleagues to ensure model interpretability and reliability based on experimental results; Design and implement rigorous evaluation frameworks testing generalization beyond IID conditions; Publish results in top-tier venues (NeurIPS, ICML, Nature, Science, Cell).
