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# Job offer

- **Company:** CNRS
- **Location:** Toulouse, France
- **Contract:** Temporary contract
- **Skills:** Infrastructure as a Service (IaaS), Machine Learning, Azure Machine Learning, Ab Initio
- **Published:** September 17, 2026
- **Apply:** https://emploi.cnrs.fr/Offres/CDD/UPR8001-ALAEST-007/Default.aspx

## About the Role

A recent PhD degree (within last three to five years) in Materials Science, noticeably Chemistry or related disciplines is required. We seek for a strongly motivated student with strong background in computational materials sciences, with skills in manipulating Density Functional Theory codes (VASP and LAMMPS mandatory) and MLP tools.

## Description

The way aluminum is reacting with an oxidative atmosphere is of major interest in a number of timely applications, i.e. propellants, energetic materials at large, cyclable green energy production … We are seeking a strongly motivated post-doctoral researcher to work on the modelling of the high temperature Al-O system.
The candidate will take in charge DFT and AIMD (ab initio Molecular Dynamics) calculations to complete the set of already available data from our group, to answer specific and mechanistic at the most fundamental level of these systems. He will further develop MLP (Machine Learning Potential) using standard techniques, including active learning procedures.
The project has several scientific objectives:
(i) Establish/consolidate a machine learning interatomic potential dedicated to the Al-O system in the Al liquid temperature window,
(ii) Study the thermochemistry of oxide nucleation and growth within liquid aluminum
(iii) Investigate the interaction of alumina aggregates with liquid Al droplets.

DFT calculations
Machine learning tools for generating interatomic potentials
Molecular Dynamics

Position is in Toulouse (LAAS-CNRS laboratory), under the supervision of A. Estève and C. Rossi, MRS team, Energy Management department


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