MSN Postdoc in Computational Catalysis, Machine Learning and Reaction Kinetics

About UM6P:

Mohammed VI Polytechnic University (UM6P) is an internationally oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa. UM6P is an institution oriented towards applied research and innovation. On a specific focus on Africa, UM6P aims to position these fields as the forefront and become a university of international standing.

More than just a traditional academic institution, UM6P is a platform for experimentation and a pool of opportunities, for students, professors and staff. It offers a high-quality living and study environment thanks to its state-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model.

In its research approach, the UM6P promotes transdisciplinary, entrepreneurship spirit and collaboration with external institutions for developing up to date science and at continent level in order to address real challenges.

All our programs run as start-ups and can be self-organized when they reach a critical mass. Thus, academic liberty is promoted as far as funding is developed by research teams.

The research programs are integrated from long-term research to short-term applications in linkage with incubation and start-up ecosystems.

About MSN:

The Materials Science, Energy, and nano-engineering (MSN) is a department of the College of Chemical Sciences and Engineering (CCSE) at Mohammed VI Polytechnic University (UM6P at Mohammed VI Polytechnic University that aims to makes use of innovative research and education in order to promote solution development and entrepreneurship (in the context of Moroccan and African challenges), while training the next generation top scientists, innovators and entrepreneurs.

Research at MSN is organized in different research clusters including Energy Transition, Surface Technology and Metallurgy, Polymers and Composites, and Sustainable Materials. With some 100 researchers and PhD students and several national and international partners, MSN is emerging as a strong actor in the Moroccan materials research scene. The department coordinates several initial and executive Master programs.

The researcher will have access to UM6P high-performance computing infrastructure and will work in an interdisciplinary research environment.

Job Description:

We are seeking a Postdoctoral Researcher to build first-principles-based microkinetic models of heterogeneous catalytic reactions, with machine-learned interatomic potentials fine-tuned on in-house DFT calculations.

The aim is to identify the elementary steps that control catalytic activity, and to predict how it responds to changes in catalyst and operating conditions, for reactions such as ammonia synthesis, ammonia decomposition and methane reforming. The energetics entering the models are computed at a single level of theory, and the potentials are used to sample coverages and configurations that are inaccessible to direct DFT.

The microkinetic models will be coupled to reactor simulations to predict conversion and selectivity under relevant operating conditions.

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 under stated operating conditions.
• Couple microkinetic models to reactor models to predict conversion and selectivity as functions of operating conditions.
• Assemble DFT training datasets covering relaxed geometries, off-equilibrium structures and transition-state configurations, and fine-tune pretrained machine-learned interatomic potentials.
• Validate the potentials against DFT calculations withheld from training, including key energies and activation barriers, and assess their reliability across relevant chemical environments and configurations and their impact on predicted kinetics.
• Use validated potentials to accelerate exploration of reaction pathways, adsorbate configurations at different coverages and configurational disorder beyond the practical 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 findings at international conferences and contribute to the supervision of Master s and PhD students.

Position Requirements:

Criteria of the candidate:

• PhD in Chemical Engineering, Chemistry, Physics, Materials Science, Energy Engineering or a closely related field.
• Experience in plane-wave DFT calculations of catalytic surfaces using Quantum ESPRESSO, VASP, CASTEP or an equivalent package.
• Experience developing microkinetic models from first-principles energetics.
• Experience coupling reaction kinetics with reactor models to predict conversion and selectivity.
• Hands-on experience fine-tuning pretrained machine-learned interatomic potentials on DFT data and validating their predictions.
• An established background in heterogeneous catalysis, electrocatalysis or energy materials.
• Python for atomistic work, including ASE and pymatgen.
• Experience running atomistic simulations on HPC systems.
• First-author publications in peer-reviewed journals reporting original computational work.
• Excellent written and spoken English, with the ability to work independently and collaboratively.
• Experience with automated or high-throughput DFT workflows would be an advantage.
• Experience with uncertainty quantification and sensitivity analysis would be an advantage.

Application and Selection

Please submit the following documents, in one pdf file:

• A cover letter outlining your research experience, computational expertise and motivation for the position.
• A curriculum vitae.
• A list of publications, which may be included in the CV.
• The names and contact details of two referees who are not UM6P faculty.

The cover letter should describe one computational project the applicant carried out personally and provide details of their ML potential fine-tuning experience, including the DFT reference data and validation results.