EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,500 people supporting the three main missions of the institution: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of more than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries.
PhD Student in Physics‑Informed Graph Neural Networks for Wind Turbine Health Monitoring 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.
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 component level in complex engineering systems, with a particular focus on wind turbines.
The research will focus on explicitly integrating physical laws, load dynamics, and degradation mechanisms into graph‑based models, enabling a principled understanding of how operating conditions drive the evolution of system health over time. Particular emphasis will be placed on spatiotemporal modeling of interacting subsystems, where degradation emerges from coupled physical processes across components.
The project will explore how graph‑based representations can capture:
the propagation of loads and stresses across interconnected components
the accumulation of fatigue and damage under variable loading conditions
the interaction between structural dynamics and degradation processes
A central aspect of the research is the incorporation of physics‑based inductive biases into learning architectures. This will enable the development of models that are physically consistent, interpretable, and robust under varying operating conditions, going beyond purely data‑driven approaches.
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:
Physics‑informed machine learning and hybrid modeling approaches
Computational mechanics, structural dynamics, or fatigue and damage modeling
Signal processing and analysis of measurement data from physical systems
Scientific machine learning or numerical methods for physical systems
Strong programming skills and the ability to work at the interface of machine learning and physics‑based modeling of engineering systems are expected.
We expect the candidate to be self‑driven, with strong problem‑solving abilities and out‑of‑the‑box thinking. Professional command of English (both written and spoken) is mandatory.
EPFL is one of the most dynamic university campuses in Europe, ranks among the top 20 universities worldwide and offers an exceptional working environment with very competitive salaries.
The IMOS Lab (https://www.epfl.ch/labs/imos/) offers a highly motivating, interdisciplinary scientific environment with many opportunities to interact across projects and researchers, and maintains an excellent network of collaborations with industrial stakeholders and leading international universities.
Application process Formal applications including:
a letter of motivation
a CV of the candidate
brief research statement (one page) describing your project idea in the field of physics‑informed graph neural networks for wind turbine health monitoring, making connections to your experience and related work from the literature
transcripts of all obtained degrees (in English)
one publication (e.g., thesis or preferably a conference or journal publication, a link is sufficient) should be submitted via the application platform.
Shortlisted candidates will be invited to apply to one of the EPFL doctoral schools (e.g. EDRS, EDCE or EDEE). This parallel application process is necessary to be eligible for a PhD at EPFL.
Informations Activity Rate: 100.00
Contract Type: CDD
Duration: 1 year contract renewable (max. 6 years)
#J-18808-Ljbffr
PhD Student in Physics‑Informed Graph Neural Networks for Wind Turbine Health Monitoring 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.
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 component level in complex engineering systems, with a particular focus on wind turbines.
The research will focus on explicitly integrating physical laws, load dynamics, and degradation mechanisms into graph‑based models, enabling a principled understanding of how operating conditions drive the evolution of system health over time. Particular emphasis will be placed on spatiotemporal modeling of interacting subsystems, where degradation emerges from coupled physical processes across components.
The project will explore how graph‑based representations can capture:
the propagation of loads and stresses across interconnected components
the accumulation of fatigue and damage under variable loading conditions
the interaction between structural dynamics and degradation processes
A central aspect of the research is the incorporation of physics‑based inductive biases into learning architectures. This will enable the development of models that are physically consistent, interpretable, and robust under varying operating conditions, going beyond purely data‑driven approaches.
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:
Physics‑informed machine learning and hybrid modeling approaches
Computational mechanics, structural dynamics, or fatigue and damage modeling
Signal processing and analysis of measurement data from physical systems
Scientific machine learning or numerical methods for physical systems
Strong programming skills and the ability to work at the interface of machine learning and physics‑based modeling of engineering systems are expected.
We expect the candidate to be self‑driven, with strong problem‑solving abilities and out‑of‑the‑box thinking. Professional command of English (both written and spoken) is mandatory.
EPFL is one of the most dynamic university campuses in Europe, ranks among the top 20 universities worldwide and offers an exceptional working environment with very competitive salaries.
The IMOS Lab (https://www.epfl.ch/labs/imos/) offers a highly motivating, interdisciplinary scientific environment with many opportunities to interact across projects and researchers, and maintains an excellent network of collaborations with industrial stakeholders and leading international universities.
Application process Formal applications including:
a letter of motivation
a CV of the candidate
brief research statement (one page) describing your project idea in the field of physics‑informed graph neural networks for wind turbine health monitoring, making connections to your experience and related work from the literature
transcripts of all obtained degrees (in English)
one publication (e.g., thesis or preferably a conference or journal publication, a link is sufficient) should be submitted via the application platform.
Shortlisted candidates will be invited to apply to one of the EPFL doctoral schools (e.g. EDRS, EDCE or EDEE). This parallel application process is necessary to be eligible for a PhD at EPFL.
Informations Activity Rate: 100.00
Contract Type: CDD
Duration: 1 year contract renewable (max. 6 years)
#J-18808-Ljbffr
PhD Student in Physics-Informed Graph Neural Networks for Wind Turbine Health Monitoring Arbeitgeber: École polytechnique fédérale de Lausanne, EPFL
L'EPFL est un employeur de choix, offrant un environnement de travail international stimulant et dynamique. En tant qu'Adjoint·e du Service académique, vous aurez l'opportunité de travailler au sein d'équipes clés, favorisant la collaboration et l'amélioration continue, tout en bénéficiant de formations de qualité et d'opportunités de croissance professionnelle. Rejoindre l'EPFL, c'est s'engager dans une institution qui valorise l'excellence et l'innovation dans le domaine académique.
Kontaktdaten:
École polytechnique fédérale de Lausanne, EPFL Recruiting-Team
