post doctoral researcher in Machine Learning

The intended research will target applications to fundamental problems in organic chemistry such as regioselectivity, optimization of the chemical yield of a reaction, minimization of by-products and finally prediction of diastereoisomeric ratios and/or enantiomeric ratios.

First, classical machine learning models will be analyzed to identify the most relevant predictive features and to build robust models of reaction outcomes. The research will then move toward geometric deep learning, leveraging graph neural networks (GNNs) to represent reacting molecules as structured objects. GNNs naturally encode both the topology of molecular graphs and the chemical properties of atoms and bonds, providing a powerful framework for predicting reaction behavior and optimizing synthetic pathways. A major scientific challenge will lie in the integration of quantum-level information into these graph-based representations. Such information is not only difficult to incorporate, but also crucial, as quantum effects fundamentally govern reaction outcomes. By targeting advanced geometric deep learning enriched with quantum information, this research is expected to advance both the theoretical foundations of molecular modeling and the practical applications of data-driven organic synthesis.

The successful candidate will work at the LITIS Laboratory in collaboration with Institut CARMeN (UMR 6064), an internationally renowned laboratory specializing in methodology development in organic synthesis.