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Post-Doctoral Research Visit F/M Multi-modal Co-Learning for sensor-specific FMs

Inria -
Montpellier (34)

Soumettre votre candidature

Détails de l'emploi

CDD
2 788 € par mois
Il y a 11 heures

Avantages

Qualifications

Français
Informatique
Niveau Doctorat
PyTorch
Anglais
Machine learning
Deep learning
Compétences en communication
Python

Description complète du poste

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

Type de contrat : CDD

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

A propos du centre ou de la direction fonctionnelle

Inria is the French National Institute for Research in Digital Science, of which the Inria Côte d'Azur University Center is a part. With strong expertise in computer science and applied mathematics, the research projects of the Inria Côte d'Azur University Center cover all aspects of digital science and technology and generate innovation. Based mainly in Sophia Antipolis, but also in Nice and Montpellier, it brings together 47 research teams and nine support services. It is active in the fields of artificial intelligence, data science, IT system security, robotics, network engineering, natural risk prevention, ecological transition, digital biology, computational neuroscience, health data, and more. The Inria Center at Université Côte d'Azur is a major player in terms of scientific excellence, thanks to the results it has achieved and its collaborations at both European and international level.

Contexte et atouts du poste

This post-doctoral position is part of the EARTH-FM project (Enhancing Archival Remote sensing data for Training Holistic Foundation Models), a joint INRIA-CNES challenge aimed at advancing the state of the art in multi-modal remote sensing Foundation Models. The project exploits large multi-temporal and multi-scale remote sensing image archives collected by CNES, such as Spot World Heritage and Pléiades World Heritage, while also exploring methodologies for data acquired through upcoming Earth Observation missions (e.g. CO3D). The work addresses challenges specific to remote sensing data, including high diversity in acquisition conditions and the integration of new modalities.

The recruited fellow will join the EVERGREEN project-team (located in Montpellier) and collaborate closely with the THOTH team (transitioning to ReSeT, Remote Sensing Team @ INRIA, in Grenoble), as well as with experts from CNES. CNES will provide technical support for data preparation on its collections and access to its HPC computing resources. The position therefore offers a rich, collaborative environment spanning multiple INRIA teams and a national space agency, with access to a unique and massive source of Very High spatial Resolution (VHR) remote sensing data covering broad geographical and temporal extents.

Mission confiée

The objective of this position is to combine self-supervised learning and multi-modal co-learning paradigms to enhance the performance of sensor-specific foundation models. Self-supervised learning will allow the model to effectively combine labeled satellite imagery with large volumes of unlabeled data, enabling the extraction of robust, task-agnostic representations. This reduces the reliance on costly manual annotation for newly acquired imagery, while keeping the model up to date with evolving Earth conditions by continuously integrating recent unlabeled data in a continual learning fashion.

To further improve generalization, the work will introduce multi-modal co-learning, enabling the model to fuse complementary information from multiple data modalities during training and ultimately improving performance on tasks that rely on a single modality. The resulting foundation models will be successively deployed and refined (fine-tuned) on a range of downstream end-user tasks, such as land use / land cover mapping, building detection, forest variable characterization, and soil property estimation.

Principales activités

The fellow will design and develop a self-supervised training framework that leverages both existing and next-generation VHR satellite imagery alongside multi-temporal Sentinel data. A central activity will be to enhance the quality and generalization capability of the resulting VHR model by incorporating rich spatial and temporal information from these diverse sources. The work will primarily leverage the Pléiades VHR dataset (and CO3D when available), in addition to SPOT 6/7 and Pléiades Neo collections.

Concrete activities include implementing and evaluating self-supervised and multi-modal co-learning architectures, integrating continual learning strategies for incorporating recent unlabeled data, and benchmarking the developed foundation models on CNES data archives across the downstream tasks listed above. The fellow will also be expected to disseminate results through publications in international journals and conferences, and to collaborate actively with the partner teams (Evergreen, THOTH/ReSeT) and CNES experts.

Compétences

Technical skills and level required: PhD in machine learning, computer vision, remote sensing, or a closely related field. Solid background in deep learning, with hands-on experience in self-supervised learning and/or multi-modal representation learning. Familiarity with modern architectures (transformers, CNNs) and with training models at scale on GPU/HPC infrastructure. Experience working with remote sensing or geospatial imagery is a strong asset, as is knowledge of continual learning and multi-temporal data analysis.

Languages: Proficiency in Python and common deep learning frameworks (e.g. PyTorch). Working knowledge of English (written and spoken) for publishing and international collaboration. French is not required but can be an asset for interaction with CNES teams.

Relational skills: Ability to work collaboratively within a distributed, multi-team environment (INRIA project-teams and CNES experts). Good communication skills to present results clearly, listen, and exchange ideas across disciplines. Autonomy, rigor, and the capacity to organize one's own research while contributing to shared objectives.

Other valued / appreciated: Track record of publications in international machine learning, computer vision, or remote sensing venues. Curiosity about Earth Observation applications and an interest in transferring research toward operational use.

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
Contribution to mutual insurance (subject to conditions)

Rémunération

Gross Salary: 2788 € per month

Informations générales

Thème/Domaine : Sciences de la planète, de l'environnement et de l'énergie
Biologie et santé, Sciences de la vie et de la terre (BAP A)
Ville : Montpellier
Centre Inria : Centre Inria d'Université Côte d'Azur
Date de prise de fonction souhaitée : 2026-11-02
Durée de contrat : 2 ans
Date limite pour postuler : 2026-07-10

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

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 : EVERGREEN
Recruteur :
Ienco Dino / [email protected]

L'essentiel pour réussir

The ideal candidate is genuinely curious about Earth Observation and motivated by the challenge of building foundation models that learn from vast, largely unlabeled archives. They feel at ease in a dynamic scientific environment spanning several research teams and a national space agency, and enjoy moving between methodological depth and concrete application. A taste for collaboration is essential.

On the technical side, solid expertise in deep learning is expected, ideally with hands-on experience in self-supervised learning, multi-modal or representation learning, and modern architectures such as transformers. Familiarity with remote sensing or geospatial data is a strong asset, as is comfort working with large-scale datasets and HPC resources. A PhD in machine learning, computer vision, remote sensing, or a closely related field is required. Beyond the technical profile, we value rigor, autonomy, scientific creativity, and the drive to publish and to see ideas through from prototype to validated method.

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'eﬀorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.

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Post-Doctoral Research Visit F/M Multi-modal Co-Learning for sensor-specific FMs

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