PhD Student in Physics-Informed Graph Neural Networks for Wind Turbine Health Monitoring — EPFL
- Location
- Lausanne
- Contract
- full-time
- Posted
- 73 days ago
Role overview
IMOS
The Intelligent Maintenance and Operations Systems (IMOS) Lab at EPFL is looking for a motivated and out-of-the-box thinking PhD researcher, (100%, in Lausanne, fixed-term) starting in September or upon agreement. Project description
The objective of this project is 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 at the compenent level in complex engineering systems, with a particular focus on wind turbines.
- IMOS
- The Intelligent Maintenance and Operations Systems (IMOS) Lab at EPFL is looking for a motivated and out-of-the-box thinking PhD researcher, (100%, in Lausanne, fixed-term) starting in September or upon agreement. Project description
Application process
- Applications will include complex industrial and energy systems, with a particular focus on wind turbines, where load conditions directly influence the degradation of critical components such as blades, gearboxes, and bearings.
- The developed methods will contribute to improving lifetime modeling, reliability assessment, and physics-informed predictive maintenance.
- This PhD position is part of an ERC Consolidator Grant, supporting cutting-edge research on physics-informed AI, intelligent maintenance, and the modeling of degradation processes in complex systems. Profile
- We are looking for a PhD candidate with a strong analytical background and an outstanding MSc degree in Mechanical Engineering, Computational Mechanics, Engineering Science, Physics, Applied Mathematics, or a closely related field.
- You should have a solid foundation in machine learning (e.g., deep learning) and mathematical modeling, including experience with dynamical systems or differential equations.
- A strong interest in modeling physical systems and degradation processes (e.g., fatigue, damage accumulation) is expected.
- Experience with graph neural networks or spatiotemporal models is highly desirable, as well as familiarity with physics-informed approaches that incorporate physical inductive bias into learning models.
- Knowledge of one or more of the following areas is considered a strong asset:
Additional details
- The Intelligent Maintenance and Operations Systems (IMOS) Lab at EPFL is looking for a motivated and out-of-the-box thinking PhD researcher, (100%, in Lausanne, fixed-term) starting in September or upon agreement. Project description
- This PhD position is part of an ERC Consolidator Grant, supporting cutting-edge research on physics-informed AI, intelligent maintenance, and the modeling of degradation processes in complex systems.
- Professional command of English (both written and spoken) is mandatory.
Notes and original content
- The Intelligent Maintenance and Operations Systems (IMOS) Lab at EPFL is looking for a motivated and out-of-the-box thinking PhD researcher, (100%, in Lausanne, fixed-term) starting in September or upon agreement.
- Project description
- Work Environment
Questions about this listing
What salary does EPFL offer for this role?
EPFL lists CHF 60'500 - 91'500 gross per year for this position in Lausanne. This is the salary published in the original listing (or, when the employer omits a figure, a realistic estimate for the role and sector) — the calculator on this site converts it to your actual net take-home once cross-border tax and social contributions are applied.
Is this a full-time role, and what type of contract does EPFL offer?
This listing is a full-time position. The contract type shown here comes directly from the employer's original posting; always confirm exact hours, notice period and probation length with EPFL during the application process, since these details can vary by role even within the same contract category.
Do I need a cross-border work permit for a role in Vaud?
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