Scientific ML for Reduced-Order PDE Simulations

An esteemed research institution in Italy is seeking a researcher to focus on scientific machine learning techniques for efficient reduced-order models. The role involves analyzing recent deep learning paradigms and applying them to computational fluid dynamics. Candidates should demonstrate knowledge of relevant machine learning and numerical methods. The application process includes an oral test to assess candidates’ aptitude for the research work. This position is part of a research fellowship funded through a national grant. #J-18808-Ljbffr