Master Internship - Graph Inference In Dynamical Systems H/F - INRIA

A propos d'Inria

Inria est l'institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l'interface d'autres disciplines. L'institut fait appel à de nombreux talents dans plus d'une quarantaine de métiers différents. 900 personnels d'appui à la recherche et à l'innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.Master Internship - Graph Inference in Dynamical Systems
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : Stage

Niveau de diplôme exigé : Bac +5 ou équivalent

Fonction : Stagiaire de la recherche

Niveau d'expérience souhaité : Jeune diplômé

A propos du centre ou de la direction fonctionnelle

The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .

The centre has , 32 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.

Contexte et atouts du poste

The aim of this internship is to investigatethe inference of graphs to describe the behavior of dynamical systems (e.g., time-series from climate database).

Subject:State-space models (SSMs) are common tools in time-series analysis for inference and prediction in dynamical systems. SSMs are versatile probabilistic models that allow for Bayesian inference by describing a (generally Markovian) latent process. However, the parameters of that latent process are often unknown and must be estimated. In [1,2],we have proposed an innovative approach to perform the parameters inference as sparse graphs. The approach, based on an Expectation-Maximization mechanism and advanced non-smooth optimization tools, provides promising results, and benefits from sound convergence guarantees. However, it is limited to the class of linear Gaussian SSMs with first-order Markovian dependancies. In this internship, we plan to explore extensions of the existing models, to cope with more complex situations (e.g., polynomial models, higher-order Markovian latent processes, non-Gaussian noise).

[1] V. Elvira and E. Chouzenoux.Graphical Inference in Linear-Gaussian State-Space Models.IEEE Transactions on Signal Processing, vol. 70, pp. 4757-4771, Sep. 2022
[2]E. Chouzenoux and V. Elvira.Sparse Graphical Linear Dynamical Systems,Journal of Machine Learning Research, vol. 25, no. 223, pp. 1-53, 2024

Mission confiée

Missions: The recruited student will firstperform abibliography study on graph dynamical models, and familarize with the existingPython codes of the team.Then, in a second step, the student will propose an extended version of the existing method, implement it, andstudy its performance on synthetic datasets.

Environment: The intern will be supervised by Emilie Chouzenoux (Head of OPIS team, Inria Saclay) and Victor Elvira (Professor, School of Mathematics, Univ. Edinburgh, UK). The intern student will join the Inria Saclay team OPIS (https://opis-inria.eu/). He/she will be located in the Centre de la Vision Numérique, in CentraleSupélec campus, Saclay, France. He/she will enjoy an international and creative environment where research seminars and reading groups take place very often. Informatic material expenses will be covered within the limits of the scale in force.

Organization: The proposed offer is dedicated to internship of Master 2 / Engineering students. The starting/end datesare flexible, with a minimum duration of 5 months.

Principales activités

Main activities:

Programming in Python or Matlab environment

Bibliographical study

Optimization problem formulation and resolution

Convergence Analysis

Scientific meetings

Writing of scientific reports

Compétences

Languages : The candidate must be fluent in english and/or french languages.

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage

Rémunération

Gratification