Modèles d'intelligence artificielle générative pour les microstructures des alliages métalliques // Generative Artificial Intelligence models for metal microstructures
ABG-132593
ADUM-66647 |
Thesis topic | |
2025-06-20 | Public funding alone (i.e. government, region, European, international organization research grant) |
Mines Paris-PSL
EVRY - Ile-de-France - France
Modèles d'intelligence artificielle générative pour les microstructures des alliages métalliques // Generative Artificial Intelligence models for metal microstructures
- Electronics
Réseaux de neurones convolutionnels, modèles de diffusion, champs aléatoires, polycristaux, propriétés mécaniques, élasticité
Convolutional Neural Networks, Diffusion Models, Random Fields, Polycrystals, Mechanical Properties, Elasticity
Convolutional Neural Networks, Diffusion Models, Random Fields, Polycrystals, Mechanical Properties, Elasticity
Topic description
L'intelligence artificielle générative connaît une véritable révolution, que la science des matériaux entend exploiter pour dépasser l'état de l'art dans de nombreux domaines. Dans cette thèse, nous proposons d'utiliser les modèles de diffusion afin de développer la prochaine génération de générateurs de microstructures d'alliages métalliques polycristallins. Le générateur IA devra non seulement produire des microstructures morphologiquement réalistes, mais aussi générer des structures compatibles avec la simulation numérique, en restituant des propriétés mécaniques proches de celles des matériaux réels.
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Generative artificial intelligence is undergoing a true revolution, and materials science aims to harness this momentum to push beyond the current state of the art in various fields. In this PhD project, we propose to use diffusion models to develop the next generation of microstructure generators for polycrystalline metallic alloys. The AI-based generator will be expected not only to produce morphologically realistic microstructures, but also to generate structures suitable for numerical simulation, reproducing mechanical properties close to those of real materials.
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Début de la thèse : 01/10/2025
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Generative artificial intelligence is undergoing a true revolution, and materials science aims to harness this momentum to push beyond the current state of the art in various fields. In this PhD project, we propose to use diffusion models to develop the next generation of microstructure generators for polycrystalline metallic alloys. The AI-based generator will be expected not only to produce morphologically realistic microstructures, but also to generate structures suitable for numerical simulation, reproducing mechanical properties close to those of real materials.
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Début de la thèse : 01/10/2025
Funding category
Public funding alone (i.e. government, region, European, international organization research grant)
Funding further details
Concours pour un contrat doctoral
Presentation of host institution and host laboratory
Mines Paris-PSL
Institution awarding doctoral degree
Mines Paris-PSL
Graduate school
621 ISMME - Ingénierie des Systèmes, Matériaux, Mécanique, Énergétique
Candidate's profile
Profil type pour une thèse à MINES ParisTech: Ingénieur et/ou Master recherche - Bon niveau de culture générale et scientifique. Bon niveau de pratique du français et de l'anglais (niveau B2 ou équivalent minimum). Bonnes capacités d'analyse, de synthèse, d'innovation et de communication. Qualités d'adaptabilité et de créativité. Capacités pédagogiques. Motivation pour l'activité de recherche. Projet professionnel cohérent.
Pré-requis (compétences spécifiques pour cette thèse) :
- Strong expertise and experience in modern machine learning
- Strong background in statistics
- Strong coding abilities, particularly in Python
- Interest in HPC for AI
- Interest in mechnical engineering and applied mathematics
- Interest in materials science
Pour postuler : Envoyer votre dossier à recrutement_these@mat.mines-paristech.fr comportant
• un curriculum vitae détaillé
• une copie de la carte d'identité ou passeport
• une lettre de motivation/projet personnel
• des relevés de notes L3, M1, M2
• 2 lettres de recommandation
• les noms et les coordonnées d'au moins deux personnes pouvant être contactées pour recommandation
• une attestation de niveau d'anglais
Typical profile for a thesis at MINES ParisTech: Engineer and / or Master of Science - Good level of general and scientific culture. Good level of knowledge of French (B2 level in french is required) and English. (B2 level in english is required) Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Teaching skills. Motivation for research activity. Coherent professional project. Prerequisite (specific skills for this thesis): Applicants should supply the following : • a detailed resume • a copy of the identity card or passport • a covering letter explaining the applicant's motivation for the position • detailed exam results • two references : the name and contact details of at least two people who could be contacted • to provide an appreciation of the candidate • Your notes of M1, M2 • level of English equivalent TOEIC to be sent to recrutement_these@mat.mines-paristech.fr
Typical profile for a thesis at MINES ParisTech: Engineer and / or Master of Science - Good level of general and scientific culture. Good level of knowledge of French (B2 level in french is required) and English. (B2 level in english is required) Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Teaching skills. Motivation for research activity. Coherent professional project. Prerequisite (specific skills for this thesis): Applicants should supply the following : • a detailed resume • a copy of the identity card or passport • a covering letter explaining the applicant's motivation for the position • detailed exam results • two references : the name and contact details of at least two people who could be contacted • to provide an appreciation of the candidate • Your notes of M1, M2 • level of English equivalent TOEIC to be sent to recrutement_these@mat.mines-paristech.fr
2025-08-31
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