Low rank matrix approximation and optimization applied to a diffusion MRI inverse problem in neurons

Télécharger l'offre au format PDF
Le descriptif de l’offre ci-dessous est en Anglais

Niveau de diplôme exigé : Bac + 4 ou équivalent

Autre diplôme apprécié : First or second year Master student or Fourth year Bachelor

Fonction : Stagiaire de la recherche

Niveau d'expérience souhaité : Jeune diplômé

A propos du centre ou de la direction fonctionnelle

The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris .

The centre has 40 project teams , 32 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris; Its activities occupy over 600 people, scientists and research and innovation support staff, including 44 different nationalities.

Contexte et atouts du poste

This internship is part of a new collaboration between Idefix Team (Inria Saclay) and UCLouvain (Belgium). The intern will be based in the Idefix Team at Saclay, France. There will be some travel between Saclay and Louvain, Belgium financed by grant.  

There will be regular meetings by video-conference with supervisers. Shared code development will be on GitHub.

 

Mission confiée

The intern will work under the supervision of Jing-Rebecca Li and collaborators at UCLouvain and Inria Saclay to apply numerical linear algebra and optimization techniques to a PDE model of the diffusion MRI signal in realistic brain cells.

The intern will solve an optimization problem to find a good low rank approximation to the forward model and then obtain statistical information about the neuron geometry.

The intern will contribute to preparing a scientific article on the results;

The forward model to compute diffusion MRI signals from realistic neurons using matrices related to the Laplace operator can be found here :

https://perso.ensta-paris.fr/~jing-rebecca.li/papers/1911.07165.pdf

The internship concerns finding a low rank approximation to the forward problem and using it to solve the inverse problem.

Principales activités

Analyze large-scale matrices;

Solve an optimization problem;

Code in Matlab or Python;

Write up results in Latex; 

Compétences

Profile sought:

Having completed coursework in Numerical Linear Algebra, Optimization, Machine Learning;

Be able to program in Matlab and Python and use GitHub;

Be able to speak and write fluently in English;

 

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

Rémunération

Gratification