Design of Aluminium Alloy Melts by machine learning for Optimising Solidification Microstructur[...]

Organisation/Company CNRS Department Sciences et Ingénierie, Matériaux, Procédés Research Field Physics Researcher Profile First Stage Researcher (R1) Application Deadline 20 Apr 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Jul 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 PhD student will be located at SIMaP laboratory in Grenoble. SIMaP ( ) is a lab hosting scientists from different disciplines working on materials science using both experiments and simulations. The PhD is part of the DIADEM National Project ( ). Two supervisors are located at SIMaP in the "campus universitaire". Grenoble, the capital of the Alpes, offers an international and simulating environment for both leisure (mountain sport) and science. Regular seminars are organized by MIAI, SPF38, and other research centers such as ESRF and ILL.

Background: The subject is part of the LUMEN project that aims at developing new aluminium alloys based on the knowledge of the liquid structure to create innovative microstructures adapted to elements inherited from aluminium recycling. The goal is to explore the addition of transition elements and their possible adjuvants in aluminium alloys to optimize their microstructures. Ab initio molecular dynamics calculations show that these modifications are linked to liquid structuring with strong fivefold symmetry. Additionally, the intensification of aluminium recycling leads to an inevitable increase in impurities, among which transition elements such as Fe, beyond the usual levels, forming precipitates that alter mechanical properties. However, some impurities could become beneficial elements: under which conditions being the aim of the research. Alloys developed for additive manufacturing show that adding slowly diffusing elements improves properties by supersaturating the matrix during solidification and slowing down precipitation in the solid state. Innovations include the deployment of combinatorial metallurgy on multi-component systems, multi-scale modeling integrating new physical models, and the use of artificial intelligence tools for upscaling between models. Identifying new products tolerant to recycling impurities will make the aluminium industry more resilient to intensified recycling.

Methodology: Atomic modeling through molecular dynamics uses interatomic potentials derived from machine learning to understand the influence of alloying elements on the liquid structure and the behavior of solid/liquid interfaces. Results of the simulations will be crucial for Phase-field modeling to predict the dynamics of solidification microstructures, considering the anisotropy of surface energy and of attachment kinetics. The link between different scales is ensured by new tools based on graph based machine learning to transfer constitutive laws from the atomic scale to the phase-field and microstructure scales. Experimental results will validate the modeling, with particular interest in ternary alloys including elements Al, Cr, Fe, Ti .

Candidate profile: We look for highly motivated candidates with a Master degree in Physics (or equivalent) and prior experience with computer simulations and a strong interest in computer science and machine learning. Students with some experience in machine learning and/or ab initio simulations are encouraged to apply. A good knowledge of written and spoken English is essential to communicate with our external collaborators in this highly collaborative project. The candidate should have some skills in programming languages (Fortran, C/C++, Python) and Linux. Basic knowledge of parallel computing will be appreciated.