Temporary Research Position Srp In Relational Learning 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.Temporary research position SRP in relational learning
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Contrat renouvelable : Oui

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Chercheur contractuel

A propos du centre ou de la direction fonctionnelle

The Centre Inria de l'Université de Grenoble groups together almost 600 people in 26 research teams and 9 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, ...), but also with key economic players in the area.

The Centre Inria de l'Université Grenoble Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

Contexte et atouts du poste

Within the framework of the GraphRec Project, our team investigates improvements to the relational learning approach.

Relational learning has recently emerged as a powerful paradigm for extracting knowledge directly from multi-table relational data by representing it as graphs and applying deep learning techniques. The position paper on \textit{Relational Deep Learning}~\cite{icml24} formalizes this vision, while benchmarks such as \textit{RelBench}~\cite{relbench} provide a systematic framework for evaluating relational representation learning methods. Foundational models like \textit{GraphSAGE}~\cite{graphsage} have demonstrated the importance of inductive approaches, yet several challenges remain unresolved.

The objective of this post-doctoral project is to design new methods for relational learning that improve over state-of-the-art.

Mission confiée

The person recruited is responsible for conducting research, taking initiatives for publishing at the highest level in data management and artificial intelligence.

Principales activités

Research and dissemination.

Avantages

- Subsidizedmeals

- 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 (90 days / year) and flexible organization of working hours

- Social, cultural and sports events and activities

- Access to vocational training

- Social security coverage under conditions

Rémunération

From 3,085 € (depending on experience and qualifications).