Research Intern on BioEmu
Job Description
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
Locations
- Berlin, Berlin, Germany, Berlin, Berlin, Germany
- Cambridge, Cambridgeshire, United Kingdom, Cambridge, Cambridgeshire, United Kingdom
Salary
Salary not disclosed
Required Qualifications
- Advanced degree or current PhD enrollment in machine learning, AI, Physics, Chemistry, biophysics, structural biology, or a related field. (degree)
- Hands‑on experience developing machine learning models. (degree)
- Proficiency in collaborative Python development on shared research codebases. (degree)
- Strong communication skills to work effectively in an interdisciplinary team and explain technical concepts to collaborators from diverse backgrounds. (degree)
- Experience working with and evaluating models such as AlphaFold and Boltz. (degree)
- Experience with diffusion models (training, sampling, evaluation). (degree)
- 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). (degree)
- MBA (degree)
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.
Travel Requirements
Fully on-site
