Atomistic and Machine-Learning Modelling of the Aluminum Combustion (M/F)

Cnrs Inc; France; Presencial; EUR 32.000 - 42.000; Jornada completa

Descripción de la vacante

CNRS, Toulouse, invites applications for a post-doctoral researcher to model the high temperature Al-O system. You will perform DFT and AIMD calculations to extend existing data and advance MLP tooling within a collaborative team.

The role focuses on developing interatomic potentials for Al-O, conducting active learning, and exploring thermochemistry in liquid aluminum, under the LAAS-CNRS Energy Management group.

Formación

• Recent PhD in Materials Science or related field.
• Strong background in computational materials science with DFT and AIMD.
• Experience with VASP and LAMMPS required.

Responsabilidades

• Model high-temperature Al-O system using DFT and AIMD.
• Develop machine learning interatomic potentials (MLP) for Al-O.
• Apply active learning to expand datasets.
• Collaborate within LAAS-CNRS team.

Conocimientos

DFT

AIMD

MLP

Machine learning

Educación

PhD in Materials Science

Herramientas

VASP

LAMMPS

Descripción del empleo

Organisation/Company CNRS Department Laboratoire d'analyse et d'architecture des systèmes Research Field Mathematics History » History of science Researcher Profile Recognised Researcher (R2) Application Deadline 13 Nov 2026 - 00:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Nov 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer 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

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.