🇨🇠PhD in Physics-Informed Graph Neural Networks
PhD in Physics-Informed Graph Neural Networks. EPFL PhD position in Physics-Informed Graph Neural Networks for Wind Turbine Health Monitoring
Location: Switzerland
Hiring Organization: EPFL
We are seeking a highly motivated PhD student in Physics-Informed Graph Neural Networks for Wind Turbine Health Monitoring at the IMOS Lab – EPFL IMOS Lab. The project aims to develop novel methodologies based on physics-informed graph neural networks (PI-GNNs) to understand and model the impact of operational loads on system degradation.
The research will focus on integrating physical laws, load dynamics, and degradation mechanisms into graph-based models. Key aspects include:
- spatiotemporal modeling of interacting subsystems
- propagation of loads and stresses across interconnected components
- accumulation of fatigue and damage under variable loading conditions
A strong analytical background and an outstanding MSc degree in a related field are required. Experience with graph neural networks, spatiotemporal models, or physics-informed approaches is highly desirable.
To apply, please submit your application via the EPFL application platform, including a letter of motivation, CV, research statement, and transcripts.
Tags: PhD position, Physics-Informed Graph Neural Networks, Wind Turbine Health Monitoring, IMOS Lab, EPFL, Machine Learning, Graph Neural Networks, Switzerland