Student Research Assistant - Scientific Machine Learning for Engineering Systems

Student Research Assistant - Scientific Machine Learning for Engineering Systems

We are looking for motivated student research assistants to join our project “Adaptive Physics-Informed Neural Operators with Reduced-Order Modeling for Complex Dynamical Systems. ” Funded through an ETH Zurich Career Seed Award, this project aims to develop scientific machine learning frameworks that integrate physics-based modeling with neural network architectures. The goal is to build efficient and robust computational tools for analyzing complex engineering systems. Applications include structural dynamics and other dynamical systems relevant to real-world engineering challenges. The work is conducted at the interface of mechanics, artificial intelligence, and computational science. The developed methods will be validated on benchmark problems and real-world data and disseminated through open-source code and scientific publications.

Start: As soon as possible (appl...