*Le descriptif de l’offre ci-dessous est en Anglais*
**Type de contrat :** CDD
**Niveau de diplôme exigé :** Bac + 5 ou équivalent
**Fonction :** Doctorant
## **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**
Thanks to advances in space observation, large, high-resolution datasets are now available for studying Earth’s climate, as well as the environments of Mars and the Moon. Extracting physical information from these datasets involves solving complex inverse problems that link measurements to their underlying causes. This PhD interdisciplinary programme focuses on Bayesian methods for estimating physical parameters from high-dimensional remote sensing data. Rather than relying on traditional assumptions, it uses modern generative AI models, particularly diffusion models, to better represent priors and sample the posterior probability distribution. The work also aims to improve inference efficiency by reducing dimensionality, reusing computations, and combining multiple measurements. The PhD candidate will develop, test and validate these methods. They will then be applied by him (her) to selected case studies for mapping the surfaces of Mars, the Moon and Earth using data from current space missions (MRO NASA, TGO ESA, EnMAP DLR).
**Context**
The spectacular development of space systems and sensors, for observing the Earth and other Planets, provides access to numerous geophysical, geochemical and biophysical parameters over vast areas with increasingly high spatial resolution and revisit frequency. Such infrastructures are crucial to **understand our changing world and climate** but also the **Martian or the Moon environments and their degree of habitability**. This involves theory but also the integration of observational information into models through data assimilation and model inversion. In this domain as in many fields of applied science, researchers face high-dimensional non-linear inverse problems. The fundamental challenge is to estimate physical parameters (the “causes,” denoted as x) from observed signals (the “effects,” denoted as y).
**Bibliography**
Douté, F. Forbes, S. Borkowski, S. Heidmann, and L. Meyer. Massive analysis of multidimensional astrophysical data by inverse regression of physical models. In GRETSI 2023 - XXIXème Colloque Francophone de Traitement du Signal et des Images, 2023.
Douté, S., Forbes, F., Borkowski, S., Meyer, L., Heidmann, S., 2024. Massive analysis of multi-angular images by inverse regression of reflectance models for the physical characterization of planetary surfaces., in: European Planetary Science Congress. pp. EPSC2024-535. https://doi.org/10.5194/epsc2024-535
Haggstrom, P.L.C. Rodrigues, G. Oudoumanessah, F. Forbes, U. Picchini. Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. Transactions on Machine Learning Research, 2024
Kugler, F. Forbes, and S. Douté. Fast Bayesian Inversion for high dimensional inverse problems. Statistics and Computing, 2021.
Nguyen, D.T., Jacquemoud, S., Lucas, A., Douté, S., Ferrari, C., Coustance, S., Marcq, S., Meygret, A., 2025. Mapping the surface properties of the Asal-Ghoubbet rift by massive inversion of the Hapke model on Pleiades multiangular images. Remote Sensing of Environment 322, 114691. https://doi.org/10.1016/j.rse.2025.114691
Eric Tatull, Silvere Gousset, Sylvain Douté, Fast inversion of hyperspectral observations using Gaussian Locally Linear Mapping, proceedings of the Société Française de Photogrammétrie et Télédétection (section hyperspectrale) SFPT – GH 2025
## **Mission confiée**
The PhD work focuses on Bayesian methods to estimate physical parameters from large volume and high dimensional remote-sensing measurements. Because these inverse problems are ill-posed, prior information is required to obtain meaningful solutions. This research aim at replacing traditional hand-crafted priors with learned priors based on modern generative AI, in particular diffusion models that can capture complex data distributions. These models are further extended to sample efficiently from the full Bayesian posterior, which is usually computationally expensive. Conditional diffusion models will be investigated as fast, data-driven alternatives to classical sampling methods. Key objectives of the PhD include implementing dimension reduction strategies, reusing computations across many observations and efficiently combining multiple complementary measurements. This will enable scalable and accurate inference for large Earth and planetary observation datasets. The development of a diffusion-based framework will also pave the way to Bayesian Optimal Experimental Design (BOED) to optimize compact spectral imagers such as ImSPOC (Imaging SPectrometer On Chip) for embedded E&S applications, including atmospheric CO₂ and CH₄ monitoring.
## **Principales activités**
This PhD work will also play a central role in developing, testing, and validating the proposed statistical and machine learning methods, and in applying them to concrete case studies in planetary and Earth observation. These applications will focus on Mars and Moon exploration, using large, multi-dimensional remote-sensing datasets product by current space missions to characterize and map surface materials (such as mineralogical composition, texture, and micro-roughness) and to quantify uncertainties. In collaboration with partner laboratories, the PhD candidate will work on selected case studies where the performance of the algorithms and the quality of the products they generate will be systematically evaluated.
**Keywords**
Bayesian statistics, AI-assisted inverse problems, planetary remote sensing, and environmental monitoring.
## **Compétences**
Strong proficiency in at least one of these domains: astrophysics, data science (statistics, inference, and machine learning), or physical remote sensing/Earth observation. Strong skills in scientific programming and digital techniques.
Scientific publications in conference proceedings or international journals. Integration of code into the xLLiM toolbox jointly developed by INRIA/Statify and IPAG (https://gitlab.inria.fr/xllim/xllim)
## **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 (after 6 months of employment) 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 under conditions
## **Rémunération**
2300 euros gross salary /month
## **Informations générales**
- **Thème/Domaine :** Optimisation, apprentissage et méthodes statistiques
Statistiques (Big data) (BAP E)
- **Ville :** Montbonnot
- **Centre Inria :** Centre Inria de l'Université Grenoble Alpes
- **Date de prise de fonction souhaitée :** 2026-10-01
- **Durée de contrat :** 3 ans
- **Date limite pour postuler :** 2026-03-05
**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**
Applications must be submitted online via the Inria website. Processing of applications submitted via other channels is not guaranteed.
**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 :** STATIFY
- **Directeur de thèse :**
Forbes Florence / florence.forbes@inria.fr
## **L'essentiel pour réussir**
The thesis will be co-supervised by Sylvain Douté (CNRS Research Director, HDR) from the PLANETO team at IPAG and Florence Forbes, Research Director and head of the Statify team at INRIA Grenoble.
IPAG is an internationally recognized research institute in planetology and astrophysics. The doctoral student will be provided with a modern laptop equipped with a GPU and funding to participate in summer schools and national and international conferences. The thesis work will also be supervised by a CNES research engineer as part of the Planetary Surfaces Data and Services Center (PDSSP). In terms of computation, we will strongly rely on the GRICAD computing infrastructure (CPU, GPU, and high performance storage).
## **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.
