Research Scientist, Frontier Health, DeepMind

• Conduct fundamental and applied ML research to develop physiological and behavioral world models simulating continuous-time human biology.
• Design novel machine learning architectures (e.g., state-space models, neural dynamical systems) for multi-modal clinical telemetry and longitudinal EHRs (e.g., MIMIC-IV).
• Build and maintain robust evaluation benchmarks (OxyBench) to assess disease trajectory predictions, acute clinical events (e.g., sepsis), and counterfactual treatment simulations.
• Collaborate cross-functionally with ML researchers, software engineers, and external clinical partners across Mountain View, London, and Paris.
• Publish original research in top machine learning conferences and leading medical journals.

Minimum qualifications:

• PhD in Computer Science, Machine Learning, Computational Biology, Applied Mathematics, Physics, or equivalent practical experience.
• 2 years of experience (industry or internships) in building world models for adaptive systems, foundation models, continuous dynamical systems, state-space models, and deep generative architectures.
• Experience with model robustness, out-of-distribution generalization, and uncertainty quantification.
• Research experience with first-author publications at machine learning venues or domain journals (NeurIPS, ICML, ICLR, etc.).

Preferred qualifications:

• Strong experience learning underlying system dynamics from partially observable environments, managing irregular sampling, missing modalities, and latent state estimation.
• Experience applying these methodologies to biomedical domains, framing multi-modal healthcare data, longitudinal EHRs (e.g., MIMIC-IV), and physiological telemetry as complex adaptive systems.
• Proficiency in Python and modern deep learning frameworks (JAX, PyTorch, or TensorFlow).
• Strong collaborative skills for working in interdisciplinary teams alongside clinical partners.
• Passion for AI technology and all of its possibilities.

• PhD in Computer Science, Machine Learning, Computational Biology, Applied Mathematics, Physics, or equivalent practical experience.
• 2 years of experience (industry or internships) in building world models for adaptive systems, foundation models, continuous dynamical systems, state-space models, and deep generative architectures.
• Experience with model robustness, out-of-distribution generalization, and uncertainty quantification.
• Research experience with first-author publications at machine learning venues or domain journals (NeurIPS, ICML, ICLR, etc.).