Overview
Microsoft Research AI for Science seeks a motivated Postdoctoral Researcher to design and lead experimental data-generation campaigns for 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 see our blog post for links to our open-source software and other resources and this explainer video.
This role is suited for researchers with either an experimental or computational background who are excited about connecting machine learning models with real-world biological measurements. They shall combine strong scientific judgement with clear communication, quantitative data interpretation and effective coordination across disciplines. The position does not include a dedicated wet-lab bench; experimental execution will primarily be carried out through external partners. This role emphasizes scientific ownership, cross-disciplinary collaboration, and scalable systems thinking, moving beyond one-off experiments or models to build reusable, high-impact data and modeling pipelines.
Why this role is exciting
You’ll be running very large-scale data generation campaigns to train next-generation AI methods that can make a meaningful impact on how biomolecular modeling is done and improve success rates in drug discovery. You provide your expertise on technical and design level, making decisions about and creating datasets that have crucial impact on our AI models. It’s an opportunity to bridge state‑of‑the‑art ML with meaningful biomedical impact in a highly collaborative research environment.
Responsibilities
1.Experimental campaign design, including areas such as
- Design scalable campaigns for biomolecular interactions, conformational dynamics and related protein measurements.
- Select systems, constructs, assays and controls based on scientific value, feasibility, diversity, throughput and cost.
- Anticipate bottlenecks and define success criteria, contingency plans and follow-up experiments.
2. CRO and external-partner leadership, including areas such as
- Translate research goals into clear work packages, milestones and experimental requirements.
- Coordinate parallel programs with CROs and academic collaborators, review progress and guide corrective iterations.
- Provide scientific direction on protein production, assay development and biophysical or structural characterization.
3. Data quality and interpretation, including areas such as
- Review raw and process
