Informations générales
Intitulé de l'offre : Postdoc (M/F) 24 months in micromechanical modeling of Cu-Cr metallic alloys (H/F)
Référence : UMR5266-VINTAU-001
Nombre de Postes : 1
Lieu de travail : ST MARTIN D HERES
Date de publication : mercredi 4 février 2026
Type de contrat : Chercheur en contrat CDD
Durée du contrat : 24 mois
Date d'embauche prévue : 1 avril 2026
Quotité de travail : Complet
Rémunération : Starting from €3041 gross monthly, depending on experience and the current salary scale of the CNRS
Niveau d'études souhaité : Doctorat
Expérience souhaitée : 1 à 4 années
Section(s) CN : 11 - Mécanique des solides. Matériaux et structures. Biomécanique. Acoustique
Missions
Modeling and numerical simulation of the plastic deformation of copper-chromium alloys. Based on experimental data, the recruited person will simulate the deformation of different Cu-Cr microstructures and link microstructures to mechanical response. The person will be able to use machine learning approaches to generate and test new microstructures in order to improve mechanical performance.
Activités
- Numerical simulations using a code based on Fourier Transforms (FFT)
- Using experimental data to generate and simulate different microstructures
- Comparing simulation results with experimental data
- Using machine learning methods to generate new microstructures
- Simulations and analyses of new microstructures and comparisons with existing ones.
Compétences
- Materials Science
- Modeling and numerical simulation
- Programming
- Artificial Intelligence
Contexte de travail
The recruited person will participate in the French collaborative research program ANR IMP3D (https://anr.fr/Projet-ANR-24-CE08-3737
). The consortium includes 4 partner labs: LMS (Paris), LEM3 (Metz), SIMaP (Grenoble), and CEA (Paris). The objective is to better understand the micromechanical links between processing, microstructures, and mechanical properties of Cu-Cr metallic alloys produced by additive manufacturing. The recruited person will perform micromechanical simulations using experimental data collected throughout the project. This includes, in particular, 2D-3D (high-resolution) electron backscatter diffraction and X-ray tomography data. The recruited person will then propose a machine learning-based strategy to generate and simulate new microstructures with targeted improved mechanical performance.
Contraintes et risques
No specific constraints or risks