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

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Offre n°2026-10649

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

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Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : Stage

Niveau de diplôme exigé : Bac + 4 ou équivalent

Fonction : Stagiaire de la recherche

A propos du centre ou de la direction fonctionnelle

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.

Contexte et atouts du poste

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.

Mission confiée

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.

Developing smarter data reduction methods

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.

References:

Luo, B. Merainani, V. Baltazart, Q. Zhang, M. Döhler. Subpixel motion estimation for video-based target-free vibration monitoring under complex environmental conditions. Mechanical Systems and Signal Processing, 2025

Luo, B. Merainani, M. Döhler, V. Baltazart, Q. Zhang. High dimensional data reduction in modal analysis with stochastic subspace identification. 22nd IFAC World Congress, 2023

Principales activités

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.

Compétences

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.

Good English communication skills.

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

Subsidized meals

Social, cultural and sports events and activities

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Informations générales

Thème/Domaine : Optimisation et contrôle de systèmes dynamiques

Calcul Scientifique (BAP E)

Ville : Rennes

Centre Inria : Centre Inria de l'Université de Rennes

Date de prise de fonction souhaitée : 2027-02-01

Durée de contrat : 6 mois

Date limite pour postuler : 2026-12-31

Attention: Les candidatures doivent être déposées en ligne sur le site Inria. Le traitement des candidatures adressées par d'autres canaux n'est pas garanti.

Consignes pour postuler

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

Sécurité défense :

Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

Politique de recrutement :

Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes en situation de handicap.

Contacts

Équipe Inria : I4S

Recruteur :

Doehler Michael /

A propos d'Inria

Inria, l'institut national de recherche dans les sciences et technologies du numérique, est en appui de l’État pour les stratégies nationales de recherche et d’innovation du numérique en tant qu'Agence de programmes. Inria mène plus de 300 projets de recherche et d’innovation avec ses 3500 scientifiques, ingénieurs et personnels d’appui, en partenariat avec les universités et l’écosystème numérique (entreprises, entrepreneurs, acteurs publics). Ensemble, nous explorons des domaines clés comme l'intelligence artificielle, la cybersécurité, l’informatique quantique, le Cloud, la transformation numérique de la santé, les jumeaux numériques ou encore les technologies numériques pour la défense. Nous construisons des solutions concrètes telles que des logiciels, des startups technologiques, des partenariats avec les entreprises du tissu national et des formations de pointe. Notre objectif : l’impact scientifique, technologique et industriel au service de la souveraineté numérique de la France.

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