By joining our team, you will have the opportunity to leverage your skills and enthusiasm in impactful projects that benefit society.

Machine learning (ML) is now commonly used in materials science to enhance the predictive capabilities of physical models. ML interatomic potentials (MLIP) trained on electronic-structure calculations have become standard tools for conducting efficient yet physically accurate molecular dynamics simulations.

More recently, generative deep-learning models are being explored to learn hidden property distributions in an unsupervised manner, and generate new atomic structures according to these distributions.

The goal of this project is to combine MLIPs and generative methods to address atomic transport properties in uranium-plutonium mixed oxides (MOX).

You will use the ML generative toolsdeveloped by our team to generate representative atomic configurations and build an ab initio database.

You will then utilize this training database to develop a new MLIP for MOX, leveraging the experience gained from developing analogous MLIPs for the corresponding pure oxides.

Finally,you will apply the new MLIP to calculate atomic diffusion coefficients, which are crucial for predicting irradiation-induced microstructure evolution and the in-reactor behavior of MOX fuels.

The work will be conducted at the Nuclear Fuel Department of CEA, within a scientific environment characterized by a high level of expertise in materials modelling, and in close collaboration with other CEA teams in the Paris region specialized in ML methods.

The results will be disseminated through scientific publications and participation in international conferences.

You have a PhDin solid-state physics/chemistry, materials science, computational physics, or a related field.

You have some expertise in machine learning.

Experience in atomic-scale modeling is a valuable additional asset.

Submit your application to join our team and contribute to CEA's groundbreaking and innovative projects!

In line with CEA's commitment to the inclusion of people with disabilities, this position is open to everyone. CEA offers worplace adaptations and/or flexible work arrangements."