Internship : Scientific calculus applied to low-rank matrices
Inria
Montbonnot-Saint-Martin, 38330
Internship : Scientific calculus applied to low-rank matrices
Le descriptif de l’offre ci-dessous est en Anglais
Type de contrat : Convention de stage
Niveau de diplôme exigé : Bac + 3 ou équivalent
Fonction : Stagiaire de l'ingénierie
A propos du centre ou de la direction fonctionnelle
The Centre Inria de l’Université de Grenoble groups together almost 600 people in 22 research teams and 7 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
Recently Inria’a Statify research team has developed a scientific library based on the GLLiM (Gaussian Locally-Linear Mapping) method applied to physical model inversion (https://gitlab.inria.fr/kernelo-mistis/kernelo-gllim-is). The approach leverages the covariance matrices associated with the GLLiM model. However, a notable drawback of this approach is the considerable computational time required when dealing with high dimensions. It is worth noting that the covariance matrices of the GLLiM model are low-rank. The idea is to exploit this property to devise more efficient algorithms.
Mission confiée
The goal of this internship is to investigate the current state of the art in algorithms for low-rank matrix calculations, including inversion, determinant computation, matrix multiplication, and log density estimation. You will conduct comparative analyses of these algorithms, assessing their efficiency, accuracy, and scalability. The goal is to 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 Functional Magnetic Resonance Imaging (fMRI).
Principales activités
Listen, learn, think, act
Compétences
- Currently pursuing a M1 or M2 degree in computer science, electrical engineering, robotics, or a related field.
- Good programming skills in C++
- Familiarity with computational statistics
- Solid understanding of mathematics, especially linear algebra and statistics.
- Strong problem-solving skills and the ability to work both independently and in a collaborative team environment.
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.)
- 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
Gratification = 4,05€ gross / hour