Postdoc in Computational Catalysis, Machine Learning and Reaction Kinetics

About the role

Mohammed VI Polytechnic University's Materials Science, Energy and Nano-engineering (MSN) department is seeking a Postdoctoral Researcher to build first-principles-based microkinetic models of heterogeneous catalytic reactions, using machine-learned interatomic potentials fine-tuned on in-house DFT calculations. The goal is to identify the elementary steps controlling catalytic activity and predict how it responds to changes in catalyst and operating conditions, for reactions such as ammonia synthesis, ammonia decomposition and methane reforming. The researcher will use the university's high-performance computing infrastructure in an interdisciplinary environment.

Key responsibilities

• Calculate adsorption energetics, reaction energies and activation barriers on catalytic surfaces using plane-wave DFT.
• Construct microkinetic models from first-principles energetics and compute rates, selectivity, surface coverages and rate- and selectivity-controlling steps.
• Couple microkinetic models to reactor models to predict conversion and selectivity under operating conditions.
• Assemble DFT training datasets and fine-tune pretrained machine-learned interatomic potentials, validating them against withheld DFT data.
• Use validated potentials to explore reaction pathways and adsorbate configurations beyond the reach of direct DFT.
• Compare predicted kinetics and reactor performance with published experimental data.
• Automate and manage 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.
• Established background in heterogeneous catalysis, electrocatalysis or energy materials.
• First-author publications in peer-reviewed journals reporting original computational work.
• Excellent written and spoken English; able to work independently and collaboratively.
• An advantage: experience with automated or high-throughput DFT workflows, uncertainty quantification and sensitivity analysis.

Required skills

• Plane-wave DFT with Quantum ESPRESSO, VASP, CASTEP or equivalent.
• Microkinetic modelling and coupling of kinetics to reactor models.
• Fine-tuning and validating machine-learned interatomic potentials on DFT data.
• Python for atomistic work, including ASE and pymatgen.
• Running atomistic simulations on HPC systems.

Applications require a cover letter describing one computational project and ML potential fine-tuning experience, a CV, a publication list and the names and contact details of two referees, all in one PDF file.