Postdoctoral Researcher, Experimental Data Generation & CRO Strategy

1.Experimental campaign design, Examples of responsibilities include:

• 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.

1. CRO and external-partner leadership, Examples of responsibilities include: * 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.

2. Data quality and interpretation, Examples of responsibilities include: * Review raw and processed experimental outputs, including binding curves and kinetic measurements. * Diagnose artifacts, failed fits and systematic assay problems using quantitative and biophysical reasoning. * Define reproducible QC criteria and scalable triage processes beyond manual review.

3. Dataset construction and model integration, Examples of responsibilities include: * Convert heterogeneous experimental outputs into traceable, model-ready datasets with appropriate metadata and provenance. * Work with computational researchers to prioritize systems, evaluate model predictions and design informative follow-up experiments. * Use basic scripting and data-analysis tools to organize, inspect and summarize experimental datasets.

4. Collaboration and research impact, Examples of responsibilities include: * Communicate experimental findings, limitations and risks to biological and computational collaborators. * Drive projects from ambiguous questions to usable datasets, scientific conclusions and publications. * Contribute to the experimental data strategy for future BioEmu models.

• PhD (or equivalent experience) in Biology, Biophysics, Biochemistry, Molecular Biology, Protein Science, Bioengineering, Computational Biology, Molecular Modelling, or a related field, with demonstrated expertise in either (a) biomolecular experimentation and quantitative assays or (b) computational methods and datasets for biomolecular systems.
• Strong quantitative understanding of experimental measurements and their limitations.
• Ability to coordinate complex projects and communicate clearly across experimental and computational teams.
• Experience working with real-world biological, structural or biophysical datasets.
• Ability to independently own and deliver research projects.

Preferred/Additional Qualifications:

• Experience managing CROs, vendors or distributed experimental collaborations.
• Expertise in protein-protein interactions, binder design, affinity optimizationor high-throughput assay development. Familiar with techniques such as protein expression and purification, binding assays (SPR, BLI, ITC, cryo-EM), structural biology (X-ray crystallography, NMR), Mass Spec (HDX-MS, Cross-link Mass Spec).
• Practical Python or equivalent scripting skills for data analysis, QC and workflow automation.
• Experience in designing, curating or standardizing datasets for machine-learning applications. Interest in model-guided experimental design, drug discovery or therapeutic applications.

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft Research AI for Science (https://www.microsoft.com/en-us/research/lab/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 (https://www.microsoft.com/en-us/research/lab/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 (https://www.science.org/doi/10.1126/science.adv9817) (see our blog post (https://www.microsoft.com/en-us/research/blog/exploring-the-structural-changes-driving-protein-function-with-bioemu-1) for links to our open-source software and other resources and this explainer video (https://www.youtube.com/watch?v=LStKhWcL0VE) ).

The successful candidate will work at the interface of structural biology, biophysics and machine learning, translating modelling needs into scalable experiments, directing work with external laboratories and CROs, and turning experimental results into reliable, model-ready datasets. This role is suited fo 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.