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

Jobs in Structural Biology and Related Fields AI for Science Postdoctoral Researcher - Biomolecular AI & Experimental Data Integration - BioEmu

Cambridge, United Kingdom

Overview

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 (see our blog post for links to our open source software and other resources and this explainer video).

The successful candidate will have the opportunity to work on the following:

• Design and scale experimental datasets for ML
• Bridge models with real world biological measurements (e.g., cryo em, binding assays)
• Develop workflows that connect noisy experimental signals to actionable model insights

Why this role is exciting

You'll work on problems that don't yet have well defined benchmarks. Where part of the innovation is deciding what to optimise and proving it matters for biology. It's an opportunity to bridge state of the art ML with meaningful biomedical impact in a highly collaborative research environment.

Responsibilities

1. Bridging Models with Real World Experimental Signals

• Develop methods to connect ML models with experimental observables, such as:
  • cryo em density maps
  • binding affinity / kinetics assays
  • proteomics / sequencing data
• Enable model inference conditioned on or steered by experimental data.
• Interpret discrepancies between model predictions and experimental outcomes to guide iteration.
• Integrate heterogeneous datasets into coherent representations for modeling.

2. Experimental Data Strategy & Dataset Development

• Design high quality, ML ready experimental datasets (e.g., protein interactions, conformational dynamics, binding measurements, cryo em density).
• Translate research questions into scalable experimental campaigns with clear success criteria.
• Define dataset standards, metadata, and quality metrics for downstream modeling.
• Identify gaps in existing datasets and propose novel data generation strategies.

3. Model Aware Experimental Design

• Establish closed loop workflows where experimental results refine models and vice versa.
• Define evaluation metrics that reflect real world biological utility, not just benchmarks.

4. Scalable Data Processing & Automation

• Build automated, reproducible pipelines for data ingestion, processing, and analysis (Python based).
• Develop systems for data curation, QC, and uncertainty estimation on noisy experimental data.
• Leverage modern tooling (databases, distributed compute, LLM assisted workflows) to scale beyond manual analysis.

5. Collaboration & External Coordination

• Partner with ML researchers and computational biologists.
• Provide technical guidance on experimental design, data quality, and iteration cycles.
• Translate between disciplines to ensure alignment between model needs and experimental outputs.
• Contribute to novel methods at the model-experiment interface.
• Publish research, release datasets/software, and shape internal research direction.
• Drive projects from ambiguous ideas to high impact, usable artifacts.

Required Qualifications

• Completed or nearly complete PhD or equivalent experience in a science or engineering discipline.
• Deep expertise in at least one relevant area, such as machine learning for biomolecular systems, molecular modeling and simulation, structural biology, experimental protein assays, or statistical mechanics.
• Strong Python skills and experience building data analysis, modeling, or machine learning pipelines.
• Experience working with real world biological, structural, experimental, or molecular datasets.
• Ability to work across disciplines and communicate complex ideas clearly.
• Track record of independently owning and delivering research projects.

Preferred Qualifications

• Experience connecting computational models to experimental data, such as cryo EM, X ray, NMR, SPR, mass spectrometry, NGS, or other assay readouts.
• Background in generative models, diffusion models, representation learning, molecular dynamics, or statistical mechanics for biomolecular systems.
• Experience with large scale dataset generation, curation, or automated analysis workflows.
• Familiarity with experimental workflows such as protein expression, purification, interaction assays, or high throughput systems.
• Interest in closing the loop between modeling and experiment.
• Experience or interest in drug discovery, therapeutics, or real world biomedical applications.
• Ability to collaborate with external partners and align research goals with practical health challenges.