💼 About This Role

You'll build machine learning algorithms to extract protein conformational dynamics directly from experimental structural biology data. You'll develop generative models that treat electron density and structure factors as training inputs, and collaborate with domain scientists to refine dynamic structural biology models. This role is part of an ambitious project aiming to make dynamic structural biology as transformative as static structures.

🎯 What You'll Do

• Develop novel algorithmic approaches to extracting protein dynamics from experimental data
• Design, train, and deploy open-source ML models learning from X-ray crystallography data
• Develop and benchmark metrics for conformational ensemble modeling
• Collaborate with domain scientists to integrate outputs with experimental pipelines

📋 Requirements

• PhD in Data Science, Computer Science, Bioinformatics, Biophysics, or related field
• Deep experience with generative models such as diffusion or flow matching
• Experience with probabilistic inference or representation learning
• Ability to work effectively in a multidisciplinary team