Research Internship In Optimization Machine Learning And Inverse Problems H/F - INRIA

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.

Research Internship in optimization, machine learning, and inverse problems
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : Convention de 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 research centre in Lyon is the 9th Inria research centre, formally created in January 2022. It brings together approximately 410 people in 20 research teams and research support services.

Its staff are distributed in Villeurbanne, Lyon Gerland, and Saint-Etienne.

The Lyon centre is active in the fields of software, distributed and high-performance computing, embedded systems, quantum computing and privacy in the digital world, but also in digital health and computational biology.

Mission confiée

Assignments:
Under the supervision of Nelly Pustelnik, Elisa Riccietti andSégolène Martin the recruited intern will develop and study multilevel plug-and-play algorithms based on flow matching for large-scale image restoration. The work will involve algorithm design, numerical experiments, and the investigation of convergence properties. For more details see the descriptions.

For a better knowledge of the proposed research subject:

The following publications provide background on the proposed research topic:

- N. Laurent, J. Tachella, E. Riccietti, and N. Pustelnik. Multilevel Plug-and-Play Image Restoration. IEEE Transactions on Computational Imaging, 12, 445-456, 2026.
- S. Martin, A. Gagneux, P. Hagemann, and G. Steidl. PnP-Flow: Plug-and-Play Image Restoration with Flow Matching. International Conference on Learning Representations, 2025.
- G. Lauga, E. Riccietti, N. Pustelnik, and P. Gonçalves. IML FISTA: A Multilevel Framework for Inexact and Inertial Forward-Backward. Application to Image Restoration. SIAM Journal on Imaging Sciences, 17(3), 1347-1376, 2024.

Collaboration:
The intern will work closely with the supervisors and interact with researchers in optimization, inverse problems, and generative modeling at ENS de Lyon.

Responsibilities:
The intern will be responsible for implementing and evaluating the proposed algorithms, analyzing numerical results, and contributing to the theoretical study. They will be encouraged to take initiatives in exploring algorithmic improvements.

Steering/Management:
The intern will organize and report on their research progress through regular meetings with the supervisors. No management responsibilities are expected.

Principales activités

Main activities (5 maximum):

- Review the literature on plug-and-play methods, flow matching, and multilevel optimization algorithms.
- Develop and implement multilevel plug-and-play algorithms incorporating flow-matching-based denoisers, and study it numerically.
- Conduct numerical experiments on image restoration tasks and compare the proposed methods with existing approaches.

Compétences

Technical skills and level required:
Strong background in applied mathematics, optimization, and machine learning. Good Python programming skills (PyTorch). Knowledge of inverse problems is appreciated.

Languages:
English or French

Relational skills:
Autonomy, scientific curiosity, teamwork, and analytical thinking.

Other valued skills:
Interest in generative models, numerical experiments, and theoretical analysis.

Avantages

- Partial reimbursement of public transport costs
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Telework possible from 2 months of internship
- Social, cultural and sports events and activities
- Leaves

Rémunération

Gratification: 4,50 euro gross / hour

• Python
• Anglais
• Machine learning
• Français