For our R&D department located in Feluy,  we are seeking a talented young researcher to accelerate the development of catalyst modeling methods with AI. The candidate will work in close collaboration with the Polymer department.

Catalysts are key materials in chemical industrial processes. They are an indispensable part of the current production processes of added value chemicals, fossil fuels, as well as sustainable fuels (i.e. CO2 and biomass).

Synthesizing and experimentally testing new catalysts is a time-consuming process. To accelerate the design of high performance and sustainable catalysts we developed an approach to numerically predict their performances combining molecular simulation (DFT) and experimental data. 

The combinatorial space of potential candidates is very large, more than 10^30. To explore this large space, reinforcement learning (RL) is an effective approach. By interacting with the environment and receiving feedback in the form of rewards, RL can identify the most promising options without the need for exhaustive search. 

The objective of this post-doctorate is to develop a custom RL methodology to accelerate the identification of new catalysts. The post-doctoral researcher will be leading this 18 month project in coordination with the project team. 

Research: – Conduct the research project, from state of the art review, designing methodology and implementation, to reporting. 

Communication: – Scientific communication internally and externally in writing or in presentations. 

Collaboration in an interdisciplinary environment involving computational chemistry, machine learning, numerical simulations, experimental chemistry. 

• PhD in Computational Chemistry,  BioInformatics or Computer Science BioInformatics  
• Familiarity with Chemistry, and especially Catalysis field. Knowledge of organometallic catalysts and polymerization is a plus. 
• Molecular Modeling: Experience in molecular modeling, particularly with Density Functional Theory (DFT) calculations  
• Machine Learning and Data Analysis: Understanding of supervised, unsupervised learning, and deep neural networks; Experience with ML for Chemistry. Experience of development of RL algorithms is a p 
• Proficiency in Python (especially with TensorFlow, PyTorch, JAX), and knowledge of parallel computing. 
• English (Fluent), French is a plus