The formation of actinide oxide nanoparticles (Th, U, Pu) in aqueous solution is a major scientific challenge, both for understanding their behaviour under environmental conditions and for better controlling the properties of future-generation nuclear fuels. The nucleation-growth mechanisms of these nanostructures remain poorly understood at the molecular level: experimental studies have revealed a growth mechanism that differs from classical nucleation theory, without being able to identify it. This thesis in theoretical and computational chemistry aims to model this process through a multiscale approach. Ab initio molecular dynamics simulations (DFT+U, VASP/CP2K) will be devoted to studying the structure, diffusion and condensation of solvated actinide(IV) hydroxo complexes, the precursors of nucleation. These results will also be used to train classical machine-learning potentials, in order to simulate large-scale systems (with LAMMPS). Biased sampling methods will finally make it possible to access rare events (high activation barriers, collective effects). The project will be carried out in collaboration with the LSFC laboratory at ICSM, who performed the experimental measurements (EXAFS/XANES, SAXS and DLS) needed to validate the models. This thesis will allow the candidate to acquire fundamental skills in ab initio simulation and machine learning. Beyond these technical skills, the candidate will also be able to develop valuable experience in scientific communication, as well as in collaborative work with experimental teams. This experience will thus be an asset for a future career, in both academia and industry.