Postdoc in Computational Catalysis, Machine Learning and Reaction Kinetics

About the role

The Materials Science, Energy, and nano‑engineering (MSN) department at UM6P seeks a postdoctoral researcher to develop first‑principles‑based microkinetic models for heterogeneous catalytic reactions, leveraging machine‑learned interatomic potentials.

Key responsibilities

• Calculate adsorption energetics, reaction energies and activation barriers on catalytic surfaces using plane‑wave DFT (Quantum ESPRESSO, VASP, CASTEP or equivalent).
• Construct microkinetic models from first‑principles energetics, compute rates, selectivity and surface coverages, and identify rate‑controlling steps.
• Couple microkinetic models to reactor simulations to predict conversion and selectivity under relevant operating conditions.
• Assemble DFT training datasets, fine‑tune pretrained machine‑learned interatomic potentials, and validate them against withheld DFT data.
• Automate reproducible calculation campaigns on HPC resources using Python‑based scientific workflows.
• Publish results in peer‑reviewed journals, present at international conferences and supervise Master’s and PhD students.

Required profile

• PhD in Chemical Engineering, Chemistry, Physics, Materials Science, Energy Engineering or a closely related field.
• Experience with plane‑wave DFT calculations of catalytic surfaces.
• Background in developing microkinetic models and coupling them to reactor models.
• Hands‑on experience fine‑tuning machine‑learned interatomic potentials on DFT data.
• First‑author publications reporting original computational work.
• Excellent written and spoken English and ability to work independently and collaboratively.

Required skills

• Python (including ASE and pymatgen).
• Plane‑wave DFT packages (Quantum ESPRESSO, VASP, CASTEP or equivalent).
• Microkinetic modeling.
• Machine‑learned interatomic potentials.
• High‑performance computing (HPC) workflow automation.