The Machine Learning group at the Heidelberg Institute for Theoretical Studies is looking for a talented PhD student with a strong background in Machine Learning who would like to work on theoretical foundations of Geometric Deep Learning with applications in biochemistry.
The PhD student will work on the theoretical foundations of Geometric Deep Learning and derive novel algorithms based on concepts such as SE3-equivariance, gauge equivariance, and graph theory. Application areas are the prediction of molecular properties and de-novo design of proteins.
Candidates should preferably have a strong theoretical background in machine learning, a solid foundation in linear algebra, and excellent programming skills. Knowledge in differential geometry, mathematical optimization, and topology are a plus. High motivation and enthusiasm to work within an international team as well as strong interpersonal and communication skills are required.
The position is fully funded with a competitive salary and starting as soon as possible.
If you are interested please apply using our career website. For general inquiries about the position please contact Jan Stühmer.