We seek passionate and highly motivated interns for the Biomolecular Emulator (BioEmu) project. 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, as well as this explainer video).
Intern Duration: 12 Weeks
Locations: Berlin, Germany Or Cambridge, UK
Responsibilities
* Design and implement machine learning models to capture protein structure, dynamics, and interactions; run ablation studies and baselines to validate ideas.
* Curate and build datasets (e.g., structural/biophysical data) and develop robust data pipelines suitable for large‑scale training and evaluation.
* Define and refine evaluation metrics/benchmarks where none exist; analyze failure modes and quantify uncertainty.
* Contribute high‑quality research code in shared Python codebases (e.g., PyTorch/NumPy/SciPy/Pandas), emphasizing reproducibility and clarity.
* Collaborate across disciplines (machine learning, structural biology, biophysics); communicate results clearly to diverse collaborators; present findings in group forums.
* Aim for impact: help translate research artifacts (models, datasets, papers, blog posts) for broader community use.
Qualifications
Required/Minimum Qualifications:
* Advanced degree or current PhD enrollment in machine learning, AI, Physics, Chemistry, biophysics, structural biology, or a related field.
* Hands‑on experience developing machine learning models.
* Proficiency in collaborative Python development on shared research codebases.
* Strong communication skills to work effectively in an interdisciplinary team and explain technical concepts to collaborators from diverse backgrounds.
Preferred/Additional Qualifications
* Experience working with and evaluating models such as AlphaFold and Boltz.
* Experience with diffusion models (training, sampling, evaluation).
* Experience designing and producing large‑scale datasets for ML (e.g., curating structural biology or biophysical datasets, establishing data quality criteria, and building scalable loaders).
Microsoft is an equal opportunity employer. Consistent with applicable law, 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 and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.