Microsoft
Postdoctoral Researcher - Biomolecular AI & Protein-Ligand / Protein-membrane interaction modeling
Get notified of more jobs like this · No spam, ever
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Overview
At Microsoft Research AI for Science, we seek highly motivated Postdoctoral Researchers on all-atom molecular dynamics for protein-ligand and/or protein-membrane interactions. 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 current role focuses on very large-scale data generation for protein-ligand and/or protein-membrane interactions using molecular dynamics (MD) simulations and free energy calculations. These data will be used for training and validating new versions of BioEmu. 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 design discovery.
- You provide your expertise on the 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.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Contract Type: Resident Researcher
Contract Length: 2 years
Responsibilities
- Design, execute, and analyze large-scale molecular dynamics (MD) simulations, including protein-ligand, protein-membrane, and free energy perturbation (FEP) studies.
- Develop high-quality, machine learning-ready datasets by translating scientific questions into scalable data generation campaigns and defining appropriate quality standards and evaluation metrics.
- Build reproducible computational workflows and automation pipelines using Python and distributed computing infrastructure.
- Collaborate with machine learning researchers, computational biologists, and research engineers to integrate simulation outputs with advanced modeling workflows.
- Design and implement model-informed experimentation strategies that leverage simulation results to improve biological modeling and predictive performance.
- Validate computational findings against reference calculations and experimental data, assessing convergence, uncertainty, and methodological limitations while troubleshooting technical challenges independently.
- Drive research projects from ambiguous scientific questions to impactful outcomes, contributing novel methods, publications, datasets, software, and broader research direction.
Qualifications
Required Qualifications
- PhD (or equivalent experience) in Computational Biology, Structural Biology, Biophysics, Physics, Statistical Mechanics, or a related field.
- Expertise in biomolecular simulation, including molecular dynamics system setup, force field selection, simulation parameterization, and related methodologies.
- Deep theoretical and practical experience with free energy perturbation (FEP), alchemical free energy methods, and assessment of convergence, uncertainty, and simulation quality.
- Experience developing scientific software and computational workflows in Python.
- Demonstrated ability to lead research projects independently and collaborate effectively across multidisciplinary teams.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Preferred Qualifications
- Experience with membrane modeling, including lipid composition selection, equilibration, and simulation setup.
- Experience integrating structural biology or molecular simulation data with machine learning approaches.
- Experience generating, curating, or managing large-scale scientific datasets.
- Experience collaborating with machine learning researchers on data generation or model development.
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer.
All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location