Nature à France

We are working with an innovative organisation at the forefront of applying AI to healthcare and life sciences. The company develops advanced foundation models and leverages multimodal data to accelerate therapeutic discovery and improve translational research.

This is an opportunity to join a highly collaborative, science-led team working at the intersection of artificial intelligence and biology.

• The chance to lead the development of next-generation generative AI models tailored for complex scientific and clinical data.
• A central role in expanding the capabilities of foundation models to simulate patient profiles, treatment responses, and disease progression.
• Collaboration with computational scientists, domain experts, and ML engineers in an interdisciplinary environment.
• The opportunity to publish in top-tier AI and scientific venues and represent the organisation at international conferences.
• A leadership position with scope to mentor junior researchers and shape the scientific direction of AI in healthcare.

• Design and implement novel generative architectures (diffusion models, VAEs, GANs, flow-based models) for large-scale biological and clinical datasets.
• Develop models that can simulate disease trajectories, therapeutic scenarios, and generate high-quality synthetic datasets for AI training.
• Drive high-impact publications and present research at leading international conferences and workshops.
• Mentor junior researchers and guide collaborative projects that translate generative AI advances into real-world applications.
• Translate breakthrough AI concepts into practical, production-ready tools that accelerate healthcare innovation.

• PhD in Machine Learning, Computer Science, Statistics, or a related quantitative field, with a focus on generative models.
• 5+ years’ research experience and a strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, Nature Methods, Nature Machine Intelligence).
• Expert-level programming in Python, with extensive experience in PyTorch or JAX, and distributed training/GPU optimisation.

• Experience applying generative AI to healthcare, biological, or other high-dimensional datasets.
• Knowledge of multimodal generative models and cross-modal data integration.
• A strong sense of ownership, adaptability, and the ability to thrive in a fast-moving environment.
• A collaborative mindset, with a drive to go beyond your job description to solve scientific and technical challenges.