En poursuivant votre navigation sur ce site, vous acceptez le dépôt de cookies dans votre navigateur. (En savoir plus)

H/F First principles validation of efficient thermoelectric materials predicted by machine learning.


Date Limite Candidature : vendredi 12 décembre 2025 23:59:00 heure de Paris

Assurez-vous que votre profil candidat soit correctement renseigné avant de postuler

Informations générales

Intitulé de l'offre : H/F First principles validation of efficient thermoelectric materials predicted by machine learning.
Référence : UMR5253-PHIJUN-002
Lieu de travail : MONTPELLIER
Pays : France
Date de publication : vendredi 21 novembre 2025
Type de contrat : Convention de stage
Durée du contrat : 6 mois
Date d'embauche prévue : 1 février 2026
Quotité de travail : Complet
Niveau de diplôme préparé : BAC+5
BAP : B - Sciences chimiques et Sciences des matériaux

Description du poste

The intern will first become familiar with the use of VASP, a state-of-the-art computational code widely employed to determine materials properties within the framework of Density Functional Theory (DFT).
The main objective of the internship is to validate, through DFT calculations, the thermoelectric properties of half-Heusler compounds that have been predicted using machine learning (ML) techniques during the Ph.D. work of Shoeb Athar.
Half-Heusler compounds crystallize in a face-centered cubic structure (space group F-43m), which makes them relatively straightforward to study computationally. These materials are particularly promising for thermoelectric applications: the thermoelectric effect allows the conversion of a temperature gradient across a solid into an electric current (and vice versa), offering potential solutions for energy recovery and efficiency.

Description de l'employeur

The internship will take place in the department of theoretical chemistry (D5) at the ICG in Montpellier.
The intern will be supervised by Pr. Philippe Jund and Dr. Shoeb Athar.

Descriptif du profil recherché

The candidate should have a strong background in materials science and quantum mechanics, with a solid understanding of the fundamental concepts underlying electronic structure methods. Prior experience with shell scripting (e.g., Bash) and/or working in a Linux environment would be an advantage but is not strictly required.
Most importantly, the intern should be motivated to work extensively with computational tools and enjoy problem-solving in a high-performance computing environment.
In addition, this internship will help the student develop valuable transferable skills, including:
• Programming and data analysis through hands-on use of VASP, BoltzTraP, and scripting tools.
• Teamwork and collaboration, by interacting with PhD students and researchers working on related projects.
• Critical thinking and autonomy, by comparing machine learning predictions with first-principles results.
This project is therefore well-suited for a motivated Master 2 student who wishes to strengthen both their expertise in computational materials science and their broader research skills.

Langues

English for all the scientific aspect, French for administrative questions.

Informations complémentaires

The internship is financially supported by the Chemistry Research Department of the University of Montpellier through a CHEM-IA grant.