About the position
Atomistic and Machine-Learning Modelling of the Aluminum Combustion (M/F)
Institution: CNRS
Research Field: Mathematics
Researcher Profile: Recognised Researcher (R2)
Application Deadline: 9 Oct 2026 - 23:59 (UTC)
Job Status: Full-time
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
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.
Machine learning tools for generating interatomic potentials
Position is in Toulouse (LAAS-CNRS laboratory), under the supervision of A.
Research Field: History
Years of Research Experience: 1 - 4
Research Field: History » History of science
A recent PhD degree (within last three to five years) in Materials Science, noticeably Chemistry or related disciplines is required.
Website for additional job details: https://emploi.cnrs.fr/Offres/CDD/UPR8001-ALAEST-007/Default.aspx
What you’ll bring
- Eligibility criteria
- A recent PhD degree (within last three to five years) in Materials Science, noticeably Chemistry or related disciplines is required.
- Research Field: Mathematics
- Education Level: PhD or equivalent
- Research Field: History
- Languages: FRENCH
- Level: Basic
- Years of Research Experience: 1 - 4
- Research Field: History » History of science
At a glance
- Position type
- Research scientist
- Institution
- CNRS
- Department
- Laboratoire d'analyse et d'architecture des systèmes
- Research group
- Not stated
- Location
- TOULOUSE, France, FR
- Supervisor / contact person
- Not stated
- Funding
- Funding not stated
Is the job funded through the EU Research Framework Programme?: Not funded by a EU programme - Duration
- Not stated
- Expected start
- Not stated
- Vacancy reference
- EURAXESS-466438
Research focus
- Discipline
- Materials Science, Chemistry
- Research area
- Not stated
- Methods
- DFT, Molecular dynamics (MD)
- Software
- VASP, LAMMPS
Dates to know
- Listed on FOSS Positions
- 17 Sep 2026
- Original advertisement date
- Not stated in the source
- Application deadline
- 09 Oct 2026 · 23:59 UTC
- Priority review date
- Not stated
- Last checked
- 17 Sep 2026
- Last updated
- 17 Sep 2026
2026-10-09T23:59:00+00:00
How to apply
Application Deadline: 9 Oct 2026 - 23:59 (UTC)
Where to apply
Apply now
https://emploi.cnrs.fr/Offres/CDD/UPR8001-ALAEST-007/Default.aspx
Original sources
https://euraxess.ec.europa.eu/jobs/466438 ↗
https://emploi.cnrs.fr/Offres/CDD/UPR8001-ALAEST-007/Default.aspx ↗
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