Internship Towards Expressive And Tractable Surrogate Models For Large Scale Inverse Problems H/F INRIA
Montbonnot-Saint-Martin - 38 Stage- 🏠 Télétravail partiel
- Bac +5
- Service public des collectivités territoriales
- Exp. - 1 an
- Exp. 1 à 7 ans
- Exp. + 7 ans
Les missions du poste
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 23 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 will BE done within anongoing collaboration between Statify Inria team, Ipag laboratory (UGA), and Inria's software development service.
Recently Inria's Statify research team has developed a scientific library based on the so-called xLLiM (Gaussian Locally-Linear Mapping) model, whose target is the resolution of Bayesian inverse problems using physical direct models and simulations from them (https://gitlab.inria.fr/kernelo-mistis/kernelo-gllim-is). In the current implementation, the model is learned from training data using a batch implementation requiring to upload all data into memory, which can limit its use to moderate volumes of data. In terms of expressiveness, the current parameterization is tailored for real-valued data and assumes only two options for the noise part of the model.
Contact : in addition to the application to the platform, more information can BE requested by contacting
Mission confiée
The goal of this internship is to extend the approach with three new functionalities, namely :
- Implementation of an incremental learning of the model parameters to allow reading the data sequentially and going beyond hardware limitations,
- Extension of the noise modelling to parsimonious parametrization by introducing an additional latent component,
- Reformulate the model with complex-valued Gaussian distributions to handle complex valued data.
These improvements should BE implemented efficiently in C++ and binded to python.
These functionalities will have to BE developed and then implemented in the current GLLiM framework (xLLiM toolbox and application PlanetGLLiM). Validation analyses of the resulting new procedures will have to BE conducted, assessing their efficiency, accuracy, and scalability. The goal is to test and improve the performance of the GLLiM model in two specific domains : space remote sensing in high-dimensional settings, and medical imaging analysis, with a particular emphasis on Magnetic Resonance Imaging (MRI).
Principales activités
- Mathematicalformulation of one or more extensions for the GLLiM method
- Implementationof the extensions in Python and in C++
- Performing tests and benchmarks
- Integrating your code into the existingxLLiM code base
- Testing for non-regression of xLLiM and PlanetGLLiM
- Writing documentation
Compétences
- Good programming skills in C++ and Python
- Familiarity with probability & statistics, eg. Gaussian mixtures, EM algorithm, Bayesian models
- Solid understanding of mathematics, especially linear algebra and optimization.
- Experience with Github, GitLab, CI, Docker
- Analytical and modeling skills : writing specifications, requirement documents, and user documentation
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
Gratification = 4,35 € gross / hour
L'adresse du poste
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Bienvenue chez 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.
- Montbonnot-Saint-Martin - 38
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