The University of Luxembourg aspires to be one of Europe’s most highly regarded universities with a distinctly international and interdisciplinary character .
It fosters the cross-fertilisation of research and teaching , is relevant to its country, is known worldwide for its research and teaching in targeted areas, and is establishing itself as an innovative model for contemporary European Higher Education.
It s core asset is its well-connected world-class academic staff which will attract the most motivated, talented and creative students and young researchers who will learn to enjoy taking up challenges and develop into visionary thinkers able to shape society.
The Faculty of Science, Technology and Medicine (FSTM) contributes multidisciplinary expertise in the fields of Mathematics , Physics , Engineering , Computer Science , Life Sciences and Medicine .
Through its dual mission of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens, in order to better understand, explain and advance society and environment we live in.
The University of Luxembourg is looking for a doctoral candidate to start working on the project Quantum machine learning for reactivity funded by the H2020-MSCA-ITN-2020 ().
The application of machine learning (ML) for chemical reaction predictions has recently gained considerable attention in the chemical industry.
Although ML models have been developed for the estimation of reactants and / or products of chemical reactions under certain conditions, less emphasis has been placed on predicting transition state features.
Moreover, a common problem in these works has been the lack of an extensive dataset, restricting the full exploration of the vast chemical reaction space (CRS) defined by reaction type, reactants, products, reaction conditions, transition states, etc.
This has also impeded further progress of ML techniques for the understanding of the CRS and the prediction of its components.
Hence, this project aims at the design and validation of novel ML-based tools benchmarked on quantum mechanical reference data of pharmaceutically relevant chemical reactions, which can then be used to accurately predict diverse components of the vast CRS emphasizing in transition state features.
The candidate will be integrated into the activities of the Theoretical Chemical Physics group lead by Prof. Alexandre Tkatchenko (
The candidate’s tasks include :
We are looking for enthusiastic candidates with a master’s degree in Physics, Chemistry, Material Science or Computer Science.
Personal initiative, the ability to work independently as well as team-oriented research and language skills (English) are expected.