PhD in Geometric Deep Learning for Materials Science at Heidelberg Institute for Theoretical Studies Heidelberg Institute for Theoretical Studies in Germany

PhD opportunity in Geometric Deep Learning for Materials Science at the Heidelberg Institute for Theoretical Studies (HITS), within the SIMPLAIX research consortium involving HITS, Karlsruhe Institute of Technology (KIT), and Heidelberg University.

The project sits at the intersection of machine learning, artificial intelligence, materials science, and mathematics. The research theme focuses on using concepts from differential geometry, group theory, and representation theory to build improved generative models that exploit the symmetry structure of crystal grids. One highlighted application is the design of novel catalysts for direct carbon air capture.

The post is especially suitable for candidates with a strong theoretical background in machine learning and a solid foundation in linear algebra. A background in chemistry is not required, but knowledge of differential geometry, group theory, and representation theory would be an advantage. The announcement emphasizes work in AI for Science and collaboration with Pascal Friederich at Karlsruhe Institute of Technology.

This is a PhD opening; no postdoc or master's opening is indicated. The post does not mention funding details or a deadline. Interested applicants should review the linked job posting for the full application process and submission instructions.