adaptyv - Intern, Protein Design
Requirements
• Currently enrolled in a Master's, PhD, or final-year undergraduate programme in computational biology, machine learning, biochemistry, biophysics, bioengineering, or a related field. • Hands-on experience with at least one modern open-source protein design method. • Strong Python and PyTorch skills. Comfortable working with biology data formats. • A working understanding of binding kinetics and developability that extends beyond in silico metrics, with a healthy scepticism of computational scores as ground truth. • Bonus: prior open-source contributions to a protein design repository, a strong showing in a public design competition, or a published blog post or paper. • Duration: 3-6 months. Paid. • Location: Remote, or on-site in Lausanne. Lausanne is preferred if you want lab access and the full design-build-test-learn exposure. • Application deadline • We are reviewing applicants on a rolling basis. Please include a link to a GitHub repository, competition submission, blog post, or other concrete work demonstrating your approach to protein design. • We are reviewing applicants on a rolling basis.
Responsibilities
• Run and benchmark open-source protein design methods on real Adaptyv targets, validated against experimental data from our wet lab. • Design binders for internal R&D campaigns, and track experimental performance across success rate, hit rate, kinetics, and developability. • Build computational tooling around the design pipeline: structure prediction, filtering, and ranking, to triage large design pools down to candidates for synthesis and characterization. • Support the technical side of our open competitions and hackathons: drafting track briefs, supporting participants, judging submissions, and writing up results. • Contribute open data, blog posts, designer spotlights, and method comparisons to Proteinbase.
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