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

IMT Nord Europe; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A research institution in France is seeking a postdoctoral researcher to focus on damage detection and health monitoring of composite materials. The role requires expertise in machine learning and time series analysis. The ideal candidate will hold a Ph.D. and possess strong skills in Python and relevant scientific libraries. This full-time position offers the opportunity to contribute to innovative research projects in a collaborative environment.

Qualifications

• Strong background in machine learning, deep learning, and time series analysis.
• Experience with signal processing, acoustic emission analysis, or sensor data interpretation.
• Proficiency in Python and relevant scientific libraries.

Responsabilités

• Developing data-driven approaches for structural health monitoring.
• Performing advanced signal and time-series analysis.
• Implementing and evaluating machine learning models.
• Contributing to scientific dissemination and project deliverables.

Connaissances

Machine learning

Deep learning

Time series analysis

Signal processing

Python proficiency

Formation

Ph.D. in Computer Science, Applied Mathematics, Mechanics, Control Engineering, or related field

Outils

PyTorch

TensorFlow

Scikit-learn

NumPy

Description du poste

Organisation/Company IMT Nord Europe Research Field Engineering » Process engineering Researcher Profile First Stage Researcher (R1) Country France Application Deadline 9 Jan 2026 - 22:00 (UTC) Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Apr 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

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.

Skills

• Strong background in machine learning, deep learning, and time series analysis
• Experience with signal processing, acoustic emission analysis, or sensor data interpretation
• Proficiency in Python and relevant scientific libraries (PyTorch/TensorFlow, Scikit-learn, NumPy, etc.)
• Ability to handle and interpret experimental data from multi-sensor systems
• Familiarity with SHM or predictive maintenance concepts is highly desirable

Qualities

• Scientific curiosity, autonomy, and initiative
• Strong analytical and problem-solving skills
• Excellent written and oral communication in English (French is a plus)