Internship: Towards expressive and tractable surrogate models for large scale inverse problems

*Le descriptif de l’offre ci-dessous est en Anglais* **Niveau de diplôme exigé :** Bac + 4 ou équivalent **Fonction :** Stagiaire de la recherche **A propos du centre ou de la direction fonctionnelle** ------------------------------------------------------- The Centre Inria de l’Université de Grenoble groups together almost 600 people in 2 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 Alpe 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** ------------------------------- This internship is part of an ongoing collaboration between the Statify research team at Inria and the Institut de Planétologie et d’Astrophysique de Grenoble (IPAG) at Université Grenoble Alpes (UGA). The internship builds on GLLiM (Gaussian Locally-Linear Mapping), a statistical modelling approach developed by the Statify team for solving Bayesian inverse problems using physical forward models and simulations. The approach is implemented in the open-source xLLiM scientific library and is also used in the PlanetGLLiM application. GLLiM provides an efficient surrogate modelling framework for inverse problems in which evaluating the physical model may be computationally expensive. However, the current implementation has several limitations. In particular, model training currently relies on a batch implementation that requires the complete training dataset to be loaded into memory, limiting its applicability to moderately large datasets. In addition, the current model parameterization is primarily designed for real-valued data that are not bounded, and provides only limited flexibility for modelling the noise component. The objective of this internship is to address some of these limitations and extend GLLiM towards more expressive, scalable, and computationally efficient surrogate models, with a particular focus on high-dimensional remote-sensing applications. **Mission confiée** ------------------- The internship will focus on developing and evaluating one or several extensions of the GLLiM framework. Depending on the candidate's background and interests, possible research directions include: * Constrained parameter estimation: incorporating physical constraints, such as bounded domains of variation for physical parameters. * Incremental learning: developing an online/incremental learning strategy that processes training data sequentially, reducing memory requirements and enabling the use of substantially larger datasets. * More flexible noise modelling: introducing an additional latent component to obtain a more parsimonious and expressive parameterization of the noise. * Complex-valued modelling: reformulating the model using complex-valued Gaussian distributions to support complex-valued observations. The selected developments will be implemented efficiently in C++ with Python bindings and integrated into the existing GLLiM ecosystem, including the xLLiM toolbox. The proposed methods will then be validated through experiments and benchmarks, with particular attention to accuracy, computational efficiency, scalability, and robustness. The ultimate goal is to improve the applicability of GLLiM to challenging, high-dimensional inverse problems arising in remote sensing. **Principales activités** ------------------------- Depending on the chosen research direction, the internship will involve: * Formulating mathematically one or more extensions of the GLLiM methodology. * Studying and implementing the corresponding algorithms in Python and C++. * Designing and conducting experiments, tests, and performance benchmarks. * Integrating the resulting developments into the existing xLLiM codebase. * Ensuring backward compatibility and performing non-regression testing. * Evaluating the accuracy, efficiency, and scalability of the proposed approaches. * Applying the methods to representative high-dimensional remote-sensing problems. * Writing technical and user documentation. * Presenting and discussing results with the research and development team. **Compétences** --------------- Required background * Currently pursuing an M2 degree or equivalent in computer science, applied mathematics, statistics, or a related field. * Good programming skills in C++ and Python. * Solid knowledge of probability and statistics, with familiarity with topics such as Gaussian mixture models, the EM algorithm, or Bayesian modelling. * Strong foundations in linear algebra and optimization. * Experience with scientific computing and statistical modelling. * Familiarity with software development practices and tools such as GitHub/GitLab, continuous integration, and Docker. Additional qualities : * Interest in the interaction between mathematical modelling, machine learning, and inverse problems. * Ability to translate mathematical concepts into robust and efficient software implementations. * Analytical and modelling skills, including the ability to formulate specifications and document technical developments. * Curiosity and willingness to work in a research environment and explore new approaches. * Ability to work independently while collaborating effectively with researchers and software engineers. * Rigorous, well-organized approach to problem solving. * Good communication and interpersonal skills. **Avantages** ------------- * Subsidized meals * Partial reimbursement of public transport costs * Possibility of teleworking (90 days / year) and flexible organization of working hours * Social, cultural and sports events and activities **Rémunération** ---------------- Gratification = 4,50 € gross / hour **Informations générales** -------------------------- * **Thème/Domaine :** Optimisation, apprentissage et méthodes statistiques Biologie et santé, Sciences de la vie et de la terre (BAP E) * **Ville :** Montbonnot * **Centre Inria :** Centre Inria de l'Université Grenoble Alpes * **Date de prise de fonction souhaitée :** 2027-02-01 * **Durée de contrat :** 6 mois * **Date limite pour postuler :** 2026-11-02 **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** --------------------------- CV + letter **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 * **Recruteur :** Borkowski Stanislaw / [email protected] **L'essentiel pour réussir** ---------------------------- Essential qualities in order to fulfil this assignment are: * Comfortable in a scientific environment, eager to learn and listen * Good listening skills and strong interpersonal abilities * Sense of initiative and responsibility * Being rigorous and well-organized * Strong problem-solving skills and the ability to work both independently and in a collaborative team environment. The internship offers the opportunity to work at the interface of research and software development, contributing to an established scientific framework while exploring new methodological developments. The candidate will have the opportunity to work closely with researchers and software engineers and to see their developments integrated into an existing research software ecosystem. **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.