Adaptive electronic-structure methods for predictive excited-state reactivity

This position involves the development of new quantum chemistry methods aimed at predicting the excited-state reactivity of transition-metal complexes. The candidate will work on improving spin-flip TDDFT approaches by integrating machine learning techniques to enhance the reliability of excited-state landscapes. Responsibilities include developing electronic structure methods, automating computational workflows, and analyzing photoinduced mechanisms. The project is part of a new research area at the intersection of quantum chemistry, computational photochemistry, and machine learning, providing a high degree of autonomy and opportunities for scientific contribution.