A completed or nearly completed PhD or comparable industry research experience is expected.
Required
Experienced in Python software development with object-oriented design and basic deployment, ideally demonstrated by published software projects (e.g., github).
Experienced in developing and implementation of deep learning systems (e.g., in PyTorch or JAX).
Experienced in modelling or simulation of molecules, ideally biomolecules.
High-quality publications in leading disciplinary or interdisciplinary journals or conferences.
Preferred
Experience with protein science or bioinformatics.
Experience with interpreting and including experimental data into models, e.g., Cryo-EM.
Understanding of computational statistics and experience with generative model development (e.g., diffusion or flow-models).
Experience in method development for molecular dynamics and statistical mechanics.
Track-record of developing and optimizing novel deep learning architectures.
Responsibilities
Contribute to and drive an ambitious, high-impact, research agenda development on machine learning in the molecular sciences.
Develop efficient and expressive machine learning models for biomolecules.
Develop strategies for integrating large-scale heterogeneous model data from molecular simulation and laboratory experiments, and identifying untapped data sources.
Write research code to test new approaches or develop novel theoretical and practical insights.
Prepare technical papers and presentations.
Working on a day-to-day basis with an international and interdisciplinary research team on one overarching research goal.
Being fully committed to one research project that may take several years to come to fruition and prioritizing team success over individual research interests.