Internship – Machine Learning and Molecular Simulation for Functional Antibody Characterisation

Description

AIDD (AI for Drug Discovery), part of Roche Computational Sciences Center of Excellence (CS-COE), is developing and applying cutting-edge machine learning to reimagine how biologic drugs are discovered and engineered. Within the Large Molecule Drug Discovery (LMDD) team, we focus on developing next-generation therapeutics, particularly therapeutic antibodies, by combining deep biological expertise with innovative computational methods.

Key areas of our interests include de novo design, structure prediction, affinity maturation, and accurate evaluation of macromolecular entities to enhance drug discovery capabilities.

The Opportunity

You will develop and evaluate novel data-driven and machine learning-based enhanced sampling methods for accurate characterisation of antibody structural ensembles.

You will deploy these methods at scale generating large-scale synthetic datasets by orchestrating the efficient usage of several available compute resources and potentially contributing to real drug-discovery projects.

You will have an opportunity to collaborate closely with our team in Basel, New York, and San Francisco.

You will have an opportunity to contribute to or drive publications, and to present your results at internal or external venues.

Who You Are

You are a recent graduated Master student (within 12 months) or You are an enrolled university student in your PhD programme in Bioinformatics, Computational Biology, Computational Physics, Computer Science, Statistics, Applied Mathematics, or related technical field.

You possess theoretical knowledge and practical experience with enhanced sampling methods or other simulation techniques and their application to protein systems.

You have extensive practical experience with at least one simulation software suite (e.g. Amber, OpenMM, Gromacs, Plumed).

You have some experience with building and training neural networks within at least one DL framework (preferably pytorch), you are keen to build models from scratch or to adapt architectures from literature.

You are fluent in high performance computing (HPC) environments and comfortable with at least one programming language (ideally Python) at the level allowing to build complex orchestrating pipelines.

You are an excellent communicator with great interpersonal skills.

Additional Information

This internship position is located in Basel, Switzerland, on site.

This is a full-time (40 hours/week) internship for a duration of 6 months.

Please note that maintained enrollment at a university for the full duration of the internship is mandatory.

Due to regulations, non-EU/EFTA citizens must provide a certificate from the university stating that an internship is mandatory as part of the application documents, and must be continuously enrolled in their university program for the whole duration of this internship.

The described project has large degree of flexibility, the exact goals and deliverables will be agreed with the successful candidate taking into the account their profile, previous experience and scientific interests.

Ready to take the next step? We'd love to hear from you. Apply now to explore this exciting opportunity!