We are looking for machine learners or chemical physicists to work on problems at the interface between machine learning and molecular physics (Postdoc or PhD level). Topics include:

• Deep learning to approximate solution of the Schrödinger equation
• Learning coarse-grained models from classical atomistic or ab-initio molecular models, e.g.:
  https://pubs.acs.org/doi/abs/10.1021/acscentsci.8b00913
• Machine learning for molecular dynamics, e.g.
  https://www.nature.com/articles/s41467-017-02388-1
• Generative deep learning for statistical mechancis, e.g.:
  https://arxiv.org/abs/1812.07669

We are looking for highly motivated colleagues with a strong theory and computational background. Please Email with CV+references and for PhD candidates with transcript to

Frank Noé, FU Berlin, Germany
[email protected]