Research Associate (Doctoral Candidate) in Molecular Machine Learning | Wissenschaftliche*r Mitarbeiter*in (Doktorand*in) (25353)

Job Summary

This role is an exciting opportunity for a Doctoral Candidate to work at the intersection of Computer Science and Mathematics, focusing on molecular machine learning within a DFG priority program. The successful candidate will develop novel machine learning methods, specifically regression models for bi-molecular properties, applied to complex systems like exciton transfer in cryptophyte antenna complexes. Day-to-day tasks involve interdisciplinary research, collaborating in an international team on topics like uncertainty quantification and high-performance computing, and contributing to teaching (4 contact hours per week) and student supervision. Key requirements include a Master's degree in a relevant scientific discipline (e.g., Computer Science, Mathematics, Physics), strong analytical skills in machine learning, and proficiency in programming (preferably Python or C/C++). This position is ideal for someone passionate about advancing novel bivariate machine learning techniques for molecular properties and seeking a structured path toward a Ph.D.