Job offer

Work environment :Computational work will be performed in strong collaboration with experimentalists expert in HT robot synthesis/characterization and advanced sorption techniques within the priority program France 2030 PEPR DIADEM AI-MAHC project (see for more details https://www.pepr-diadem.fr/projet/ai-mahc-2/). The Postdoc will work in tandem with a PhD student and will benefit from the PEPR DIADEM infrastructure, including advanced platforms, as well as from extensive scientific exchanges enabled by dedicated schools, workshops, and collaborative activities.

Our research group: The successful candidate will join a strongly connected and international research team and collaborate with national and international academic partners. Our group is internationally renowned in the field of computational studies of MOFs (see for more information https://scholar.google.com/citations?hl=fr&user=QNfwyjgAAAAJ&view_op=list_works&sortby=pubdate

Main mission : The overall objective of the project is to devise Artificial intelligence (AI)-driven strategy to discover a new generation of MOF water adsorbents with optimal indoor air humidity control performance by leveraging state-of- the-art high-throughput (HT) computational screening based on Machine-Learning Interatomic Potentials (MLIP), machine learning (ML) predictive models and AI tools.

Activities : Computer science implying ML and AI tools applied to material science

PhD or equivalent, We are looking for a highly motivated Postdoc candidate with a PhD degree in computer science, physical chemistry, chemical physics, theoretical physics, or a related field, with a strong background in training GNN-based ML predictive models (Equiformer, GemNEt, eSEN…..) for accurately predicting Material properties (especially adsorption properties). Experience with ML models applied to materials science alongside background in AI-generative diffusion models (MatterGen, MOFGen…) is a clear advantage.