Research Internship: Video-Based Vibration & Uncertainty

Inria Rennes; Rennes; On-site; EUR 12,000 - 18,000; Full time

Job summary

Inria Rennes invites a Master’s student (M2) or final-year engineering student for a 5–6 month internship at the Rennes center. The project focuses on video-based motion measurement for structural health monitoring, addressing uncertainty quantification and smart data reduction to improve reliability and efficiency of SHM algorithms.

You will work under supervisors and collaborate with the I4S team, contributing to methods that extract meaningful insights from large video datasets for vibration

Qualifications

• Strong background in applied mathematics, signal processing or statistics.
• Interest in developing algorithms and applying mathematical methods to real-world problems.
• Programming experience in Python, MATLAB, Julia, or equivalent.
• Good English communication skills.

Responsibilities

• Literature review on uncertainty quantification and dimensionality reduction for video-based motion measurements.
• Analysis of video-derived motion signals and their statistical properties.
• Development and implementation of uncertainty quantification methods.
• Design and implementation of dimensionality reduction algorithms for signal-to-noise optimization.
• Validation and performance assessment using experimental data and existing identification algorithms.

Skills

Applied mathematics

Signal processing

Statistics

Python

MATLAB

Julia

English communication

Research interest

Education

Master’s student (M2)

Final-year engineering student

Tools

Python

MATLAB

Julia

Job description

Inria Rennes invites a Master’s student (M2) or final-year engineering student for a 5–6 month internship at the Rennes center. The project focuses on video-based motion measurement for structural health monitoring, addressing uncertainty quantification and smart data reduction to improve reliability and efficiency of SHM algorithms.

You will work under supervisors and collaborate with the I4S team, contributing to methods that extract meaningful insights from large video datasets for vibration