Uncertainty-Aware Video Processing Intern for SHM

Inria; Rennes; On-site; EUR 10,000 - 15,000; Full time

Benefits offered by this job

Social, cultural and sports events

Job summary

Inria Rennes, within the I4S research team, invites Master students (M2) to perform a 5-6 month internship focused on uncertainty-aware video processing for Structural Health Monitoring. The project explores estimating measurement reliability without ground-truth references and reducing data size while preserving signal quality.

Collaboration spans with Gustave Eiffel University and laboratories, under senior supervision.

Qualifications

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

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 aiming at signal-to-noise ratio optimization.
• Validation and performance assessment using experimental data and existing identification algorithms.

Skills

Applied mathematics

Signal processing

Statistics

Python

MATLAB

Julia

Education

Master student (M2)

Engineering student

Tools

Python

MATLAB

Julia

Job description

Inria Rennes, within the I4S research team, invites Master students (M2) to perform a 5-6 month internship focused on uncertainty-aware video processing for Structural Health Monitoring. The project explores estimating measurement reliability without ground-truth references and reducing data size while preserving signal quality.

Collaboration spans with Gustave Eiffel University and laboratories, under senior supervision.