Contexte et atouts du poste
This internship will be done within an ongoing 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 ( 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:
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
Compétences
Avantages
Rémunération
Gratification = 4,35 € gross / hour