Cette offre n'affiche pas de salaire. D'après 40 offres pour ce poste (tous niveaux, Île-de-France), le marché se situe autour de 55k€ (46k€–63k€).
Description du poste
Domaine
Mathématiques, information scientifique, logiciel
Contrat
Post-doctorat
Intitulé de l'offre
Using generative AI to simulate chemically disordered nuclear materials at the atomic level H/F
Sujet de stage
This postdoc aims to enhance PULSE, a generative (VAE) method revolutionizing predictions for disordered materials. You'll improve its accuracy, uncertainty quantification, and transition to a continuous latent space within a CEA consortium combining nuclear physics and AI.
Durée du contrat (en mois)
24
Description de l'offre
How do you predict a material's properties when the number of possible atomic configurations exceeds 2^2500? That is the bottleneck our IRESNE (nuclear fuel physics) and LIST (AI) teams have just cracked with PULSE, a generative (VAE) method published in Nature Scientific Reports, already cutting computational cost by more than two orders of magnitude (22,282 CPU hours down to 85 on a test case). With no known equivalent in the international literature, PULSE positions CEA as a pioneer in generative sampling of the configuration space of chemically disordered materials.
This 24-month postdoc gives you the opportunity to drive this method toward its next generation, leading three ambitious, parallel research axes: pushing model accuracy on systems of several thousand atoms with an IWAE architecture; equipping it with the ability to quantify its own uncertainty - a prerequisite for any use in nuclear safety; and, in the second year, tackling a high-value exploratory axis - generalizing PULSE to a continuous latent space, opening the door to any disordered crystal or alloy.
You will work at the heart of an all-CEA consortium bringing together two complementary strengths - atomistic nuclear fuel physics at IRESNE and state-of-the-art generative AI at LIST - with access to CEA supercomputers, the freedom to publish in top-tier journals, and the prospect of seeing your results feed directly into reactor safety analyses through the PLEIADES platform.
A position built for a curious mind who wants to combine cutting-edge generative AI research with concrete impact on a strategic nuclear-energy challenge.
Profil du candidat
You hold a PhD in Physics, Applied Mathematics, or Computer Science, with experience in deep generative models (VAE, diffusion) and Python/PyTorch.
Applications from statistical physicists are welcome.
The additional benefits of your mission might interest you:
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