Master internship: Uncertainty-Aware Video Processing for Structural Health Monitoring

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

Master internship: Uncertainty-Aware Video Processing for Structural Health Monitoring

The Inria center at the University of Rennes is one of eight Inria centers and has more than thirty research teams. The Inria center is a major and recognized player in the field of digital sciences. It is at the heart of a rich ecosystem of R&D and innovation, including highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education institutions, centers of excellence, and technological research institutes.

Inria is the French National Institute for Research in Digital Science and Technology. As a leading public research institution, Inria plays a key role in driving digital transformation in France. Its core activities include research in computer science, mathematics and artificial intelligence (AI), software development, innovation in technology-intensive fields, and DeepTech entrepreneurship. Inria ranks 16th worldwide in AI research and is Europe's leading institute for exploratory research in digital science and technology.

The internship will take place at the Inria Centre at the University of Rennes, within the I4S research team , under the supervision of Michael Doehler and Qinghua Zhang . It will be carried out in collaboration with Gustave Eiffel University (Nantes campus, SII laboratory), under the supervision of Vincent Baltazart.

Can we estimate how reliable a measurement is without knowing the true value of the measured quantity? And how can we extract useful information from high dimensional noisy data for a given purpose?

Video-based motion measurement offers a promising way for Structural Health Monitoring (SHM) of bridges, buildings, and wind turbines without installing physical sensors on the structures. By processing video recordings, it is possible to extract motion signals at thousands of points, providing a remarkably detailed picture of structural vibration behavior. However, two major challenges remain: assessing the reliability of these signals and efficiently processing the resulting large datasets. This internship will address both challenges by developing methods at the intersection of signal processing, uncertainty quantification, and mathematical optimization.

• Estimating measurement uncertainty without reference sensors

Video-processing algorithms can extract motion signals at thousands of image locations, effectively creating a dense network of virtual sensors. Yet, assessing the accuracy of these signals usually requires reference measurements from high-precision physical sensors, something that is often unavailable in real-world applications. You will investigate how the redundancy between neighboring virtual sensors can be exploited to estimate measurement uncertainty directly from video-derived signals, without requiring ground‑truth measurements.

Processing thousands of motion signals can be computationally expensive and may introduce unnecessary noise into subsequent analyses. You will develop dimensionality reduction techniques based on orthogonal projections that maximize the signal-to-noise ratio. The objective is to retain the most useful information while reducing data size and improving the reliability of subsequent SHM algorithms.

• 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 of the proposed methods using experimental data and existing identification algorithms.

We are looking for a motivated Master’s student (M2) or final-year engineering student with:

• A solid background in applied mathematics, signal processing, statistics, or a related field.
• An interest in developing algorithms and applying mathematical methods to real-world problems.
• Programming experience in Python, MATLAB, Julia, or an equivalent language.
• Curiosity, scientific initiative, and an interest in pursuing research, potentially through a PhD.

Duration: 5-6 months (flexible start date between January and April 2027)

Applications must include the following documents:

• A detailed CV
• A transcript of records for the Master’s degree
• A cover letter explaining the applicant’s motivation and detailing relevant elective courses, specializations and projects related to the internship topic

Avantages

• Social, cultural and sports events and activities

Please submit online : your resume, cover letter and letters of recommendation eventually