Thesis: Advanced Modeling and Optimization Using Artificial Intelligence for Dry-Process Electrode Manufacturing (M/F)

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description The research will focus on numerical simulation and multiphysics modeling, including the development and implementation of models based on the discrete element method (DEM). These models will enable the analysis of granular dynamics, powder mixing, and particle interactions at the mesoscopic scale. In addition, models based on the finite element method (FEM) will be developed to simulate the mechanical behavior, compaction, and final structure of the electrodes at the macroscopic scale. This modeling of the various process steps will rely on expert use of the LIGGGHTS, LAMMPS, and COMSOL Multiphysics software packages. Process optimization will be carried out in close collaboration with the research consortium. The project will involve the collection, organization, and analysis of experimental data provided by the various partners. A data-driven optimization loop will be developed to correlate numerical modeling parameters with experimental results. The ultimate goal will be to identify optimal parameter sets—such as formulation, pressure, or velocity—in order to significantly improve the efficiency of the dry process and guide the consortium's future experimental campaigns. As part of the development of new battery technologies, the PhD student will be tasked with designing a multiscale modeling framework dedicated to the dry-process fabrication of electrodes. This work aims to sim