At the Institute of Applied Sciences and Simulation for Low-Carbon Energies (ISAS) of the CEA, we focus on research and innovation in analytical sciences. As data analysis plays a pivotal role, we are interested in methodological advancements in statistics, mathematics and computer science, for instance, via the development of state-of-the-art AI models, adapted to our needs.

The Critical Heat Flux (CHF) is a physical phenomenon that may cause the deterioration of the heat transfer in the core of nuclear reactors, potentially leading to core damage. Its accurate prediction is therefore a crucial issue in nuclear reactor safety. The internship deals with the prediction of the CHF through the development of machine learning techniques and the quantification of the associated uncertainty. It will be realised in the context of an international OECD/NEA project on the development and application of AI methodologies for nuclear engineering.

Within this project, the intern will:

• Perform a preliminary literature review on potential candidate artificial intelligence methodologies (such as world models and latent space alignment architectures).
• Analyse the available experimental CHF database to understand the physical phenomenon and to verify the coherence and exploitability of the experimental data.
• Perform an initial assessment of basic machine learning techniques applied to the prediction of CHF.
• Develop different architectures to predict as accurately as possible the CHF for various geometries, e.g. annular tubes or rod bundles.
• Quantify the uncertainty of the models developed during the internship, applying different state-of-the-art methodologies such as conformal prediction, hierarchical models and other coverage-driven techniques.
• Present and discuss their work during technical meetings and write a final report.

The internship will be conducted at the Service of Software Engineering and for Simulation (SGLS). The service develops and qualifies the simulation tools for enhancing the precision and reliability of the models used to design and analyse the safety of the French nuclear power systems. These studies are performed within the framework of internal CEA projects or in collaboration with several industrial partners (e.g. EDF, Framatome and TechnicAtome) and international partners. The internship will be a joint operation with the Service of Thermal-hydraulics and Fluid Dynamics (STMF).

The ideal candidate is a MSc. student (French M2 level) who has a good knowledge of statistics/applied mathematics with an interest in artificial intelligence and neural networks. Basic knowledge of the physics processes underlying the CHF phenomenon will be considered a plus. Good skills in Python coding and code management are required. We welcome previous experience with any major deep learning framework (PyTorch, JAX, Tensorflow) and with uncertainty quantification approaches. Given the international context of the OECD/NEA project, fluency in English is considered necessary (French will be considered a plus).