Engineering design optimization faces significant challenges when dealing with computationally expensive simulations (ranging from 10 minutes to several days per evaluation) and inherent uncertainties in operating conditions and manufacturing tolerances. Traditional deterministic optimization approaches often yield designs that are sensitive to these uncertainties, leading to performance degradation or constraint violations in real-world conditions.
The need to balance multiple objectives while satisfying probabilistic constraints, such as reliabil-ity requirements, is paramount in industries like aerospace, automotive, and energy. This intern-ship focuses on enhancing state-of-the-art robust optimization methodologies to address multi-objective problems under uncertainties, with a particular emphasis on handling low-probability events and accelerating the optimization process using advanced deep learning techniques.
Catégorie d\'emplois: Employment PMM
Internship M1/M2 – 3D and 4D printing of biocomposites for deployable structure
Post-Doctoral fellowship in multi-scale modeling of the growth of polycrystalline thin films.
The objective of this post–doctoral position is to model the growth and microstructural evolution of polycrystalline metallic thin films by developing a numerical simulation code based on kinetic Monte Carlo (kMC) which considers the specificities of energetic physical vapor deposition (PVD) such as magnetron sputtering. It is part of the DREAM project funded by ANR and Région Nouvelle Aquitaine. To this end, the candidate will have to link the deposition parameters (deposited energy, particle flux, substrate temperature and the chemical reactivity at the substrate interface to the microstructure (grain size, texture) and morphology (roughness, faceting) evolution of the growing layer. Specific attention will be also placed on the creation of defects due to energetic bombardment in the polycrystalline metallic layer.