3 Design positions at IFP Energies nouvelles (IFPEN) in phd

PhD in Applied Mathematics: Active learning with functional inputs: application to wind turbine reliability design

model is generally built adaptively by an active learning methodology, from simulations associated with an initial design of limited size. This design is then enriched using criteria adapted

PhD in Data Science: Real-Time Data-Driven Modeling of Road Traffic Pollutant Emissions and Atmospheric Concentrations at an Urban Scale

simulations, which can represent significant computational time. To address this, we plan to introduce deep learning algorithms to develop accelerated models for determining pollutant concentrations in

PhD in Applied Mathematics: Neural Linear Solvers and Preconditioners for General Sparse Matrices

crucial for research in several scientific fields, from Computational Fluid Dynamics (CFD) for aeronautics design to Darcy flow in porous media for CO2 storage and geothermal energy. The performance