AI for Science Postdoctoral Researcher - Biomolecular AI & Experimental Data Integration

At Microsoft Research AI for Science we seek highly motivated Postdoctoral Researchers for experimental data integration into the next Biomolecular Emulator (BioEmu) model.

Microsoft Research AI for Science focuses on the development of machine learning and artificial intelligence methods for transforming molecular simulation and discovery of novel materials, drugs and chemical reactions. The BioEmu project aims to model the dynamics and function of proteins — how they change shape, bind to each other, and bind small molecules. This approach will help us to understand biological function and dysfunction on a structural level and lead to more effective and targeted drug discovery. Our BioEmu-1 model was published in Science.

Key Responsibilities

• Bridging Models with Real-World Experimental Signals: Develop methods to connect ML models with experimental observables including cryo-EM density maps, binding affinity/kinetics assays, and proteomics/sequencing data.
• Experimental Data Strategy & Dataset Development: Design high-quality, ML-ready experimental datasets and translate research questions into scalable experimental campaigns.
• Model-Aware Experimental Design: Establish closed-loop workflows where experimental results refine models and vice versa.
• Scalable Data Processing & Automation: Build automated, reproducible pipelines for data ingestion using Python-based tools.
• Collaboration & External Coordination: Partner with ML researchers, computational biologists, and experimental collaborators.
• Independent Research & Impact: Contribute to novel methods and publish research.

Required Qualifications

• Completed or nearly complete PhD in science or engineering
• Deep expertise in machine learning for biomolecular systems, molecular modeling, structural biology, or related areas
• Strong Python skills for data analysis and modeling pipelines
• Experience with real-world biological or molecular datasets
• Ability to communicate across disciplines
• Track record of independently owning research projects

Preferred Qualifications

• Experience connecting computational models to experimental data (cryo-EM, X-ray, NMR, SPR, mass spectrometry)
• Background in generative models, diffusion models, or molecular dynamics
• Experience with large-scale dataset generation and curation
• Familiarity with experimental workflows such as protein expression and purification
• Interest in closing model-experiment loops
• Drug discovery or biomedical applications experience

Locations: Cambridge, United Kingdom and Berlin, Germany.