About the FSTM The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg 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. Your role A PhD position is available in the Theoretical Chemical Physics (TCP) group, led by Prof. Alexandre Tkatchenko. This PhD position belongs to PHYMOL: A Marie Sklodowska Curie Actions Doctoral Network (MSCA DN) on intermolecular interactions. As such, the PhD candidate will enjoy a broad collaboration with world class research groups. Dr. Marcus Neumann, CEO of the world leading company in crystal structure prediction, Avant garde Materials Simulation Deutschland GmbH, will co supervise this PhD project. The TCP group also routinely collaborates with Google DeepMind and leading pharmaceutical companies. Machine learned force fields (MLFF) can reproduce the potential energy surfaces of small molecules in vacuum with the accuracy of ab initio methods, such as density functional theory (DFT), at orders of magnitude reduced cost [1]. An open problem now is how to extend these methods to the liquid and solid phase. The main problem being that it is difficult to train or integrate long range interactions into these force fields to reproduce intermolecular interactions. If this can be done, numerous applications become possible, such as the prediction of effective therapeutic molecules and the stable molecular crystal phases of these molecules [2]. The goal of this project is to develop novel ways to test the ability of machine learned force fields (MLFF) to capture long range intermolecular interactions via comparison to DFT with the many body dispersion correction [3] and then to seek ways to improve the errors discovered. Day to day: The research will involve using a high performance computing cluster to run DFT codes and test and develop MLFF methodologies. The student will be co supervised by Dr. Dahvyd Wing and is expected to interact and collaborate with other PhD students and postdocs in the TCP group. Your profile We are looking for highly motivated candidates with: A Masters degree (or equivalent degree) in Chemistry, Physics, Material Science or Computational Science Excellent programming skills and mathematical ability A good understanding of quantum mechanics and physical/chemical intuition Good command of written and spoken English Experience with machine learning is especially favorable For more information contact: Prof. Dr. Alexandre Tkatchenko () We offer Multilingual and international character. Modern institution with a p