Understanding and modeling magnetohydrodynamic (MHD) dynamos—flows of electrically conducting fluids that spontaneously generate magnetic fields—remains a central challenge in geophysics and astrophysics. These systems exhibit multi-scale, turbulent behavior, and their numerical simulation produces massive 3D datasets that are difficult to store, interpret, and reuse for modeling and data assimilation. The ANR project MilaDy aims to bridge this gap by developing machine-learning models that respect the underlying physics of MHD flows. The internship will focus on the development of physics-aware data representations that compress turbulent dynamo states while preserving their essential physical structures (energy distribution, coherent flow patterns, and parameter dependencies).