Context :

The research will be carried out at the French National Research and Safety Institute for the Prevention of Occupational Accidents and Diseases (INRS) in Nancy (FRANCE), in collaboration with the FEMTO-ST Institute in Besançon (FRANCE). The work will involve developing a model that reproduces the dynamic behavior of machine seats (loaders, dumpers, etc.) based on acceleration measurements. The originality of this thesis lies in combining a structural dynamics model with machine learning methods derived from artificial intelligence (neural networks, etc.) to simulate complex nonlinear systems.

Machine operators (dumpers, loaders, forklifts, etc.) are exposed to vibrations that can lead to low back pain. Machine seats are equipped with suspension mechanisms designed to reduce this exposure. To prevent occupational risks, INRS has launched an ambitious scientific study aimed at improving the vibration isolation of seat suspensions. This study will involve in-situ assessment of the vibration insulation performance of seats used in the workplace

Required Profile  :

Master’s student (second year) or engineering school student in mechanics, system dynamics, or a related field.

Technical skills: Numerical modeling of physical phenomena, vibration and mechanical analysis, signal processing methods, programming (Python or MATLAB). Knowledge of machine learning methods would be appreciated.

Personal qualities: Scientific curiosity and rigor, analytical mindset, autonomy, and ability to work in a team.

Bonus: Interest in laboratory experiments and applications with real-world impact.