Postdoctoral Researcher in Structural Health Monitoring (SHM) and Embedded Predictive Maintenance

CONTEXT :

Public establishment belonging to IMT (Institut Mines-Télécom), placed under the supervision of the Ministry of Economy, Finance and Industrial and Digital Sovereignty, IMT Nord Europe has three main objectives: providing our students with ethically responsible engineering practice enabling them to solve 21st century issues, carrying out our R&D activities leading to outstanding innovations and supporting territorial development through innovation and entrepreneurship. Ideally positioned at the heart of Europe, 1 hour away from Paris, 30 min from Brussels and 1h30 from London, IMT Nord Europe has strong ambitions to become a main actor of the current industrial transitions, digital and environmental, by combining education and research on engineering and digital technologies.

Located on two main campuses dedicated to research and education in Douai and Lille, IMT Nord Europe offers research facilities of almost 20,000m² in the following areas:

• Digital science,
• Energy and Environment,
• Materials and Processes.

For more details, visit the School’s website: www.imt-nord-europe.fr

The postdoctoral position is offered within the Digital Systems centre, which bridges the physical and digital worlds by modelling and optimizing complex systems, enhancing human–machine interactions, and designing secure, connected systems. The centre is committed to academic excellence and focuses on innovative, high-impact research addressing pressing societal and technological challenges. Its work spans the full spectrum of Technology Readiness Levels (TRLs), enabling both fundamental insights and applied solutions.

This position is part of the Maghydro project, which aims to improve the safety, reliability, and lifetime prediction of pressurized hydrogen storage systems through advanced monitoring and data-driven approaches. The project combines experimental testing, damage characterization, and predictive modeling to develop embedded Structural Health Monitoring (SHM) and predictive maintenance solutions.

Within this framework, Maghydro focuses on several key objectives:

• Pressure testing (static and fatigue) of instrumented composite bottles equipped with strain gauges, accelerometers, and acoustic emission (AE) sensors,
• Characterization of damage mechanisms through acoustic emission, strain damage and accelerometer analysis,
• Assessment and classification of defects, including manufacturing defect criticality and progressive damage evolution,
• Health monitoring of in-service bottles, leading to the development of embedded predictive maintenance systems.

The postdoctoral researcher will contribute to this effort by designing and validating data analysis and machine learning methods for damage detection, fault diagnosis, and health indicator estimation, using data collected from both laboratory and in-service experiments. He/She will be responsible for:

• Developing data-driven approaches for structural health monitoring, by leveraging multi-sensor data (strain, acoustic emission, pressure, accelerometer, etc.) collected from instrumented bottles during tests and in-service operation.
• Performing advanced signal and time-series analysis to:
  • Detect anmalies and early signs of damage,
  • Characterize acustic emission events and relate them to damage mechanisms,
  • Estimate health indicatrs representative of structural integrity.
• Implementing and evaluating machine learning and deep learning models (including weakly or semi-supervised approaches) for:
  • Fault diagnsis and classification of defects,
  • Prgnostics and Remaining Useful Life (RUL) estimation.
  • Integrating mdels into an embedded predictive maintenance framework, ensuring real-time applicability and robustness to environmental variability.
• Contributing to scientific dissemination and project deliverables:
  • Writing scientific papers and reprts,
  • Presenting results in prject meetings and conferences,
  • Cllaborating with experimental and modeling teams within the Maghydro consortium.

Required Degree: Ph.D. in Computer Science, Applied Mathematics, Mechanics, Control Engineering, or a related field.