Large-scale sparse blind decomposition

The goal of this internship is to work on large scale sparse blind source separation and more specifically to contribute to the development of block-coordinate and mini-batch methods, while merging these approaches into a general framework.

Details about this position are provided in the following PDF.

Position: Internship

Deadline:  28/02/2019

Contact: Christophe Kervazo.  

Cosmological Parameters Estimation within Growing Neutrinos

Position: Internship
Deadline:  28/02/2019
Contact: V. Pettorino and S.Casas

Dark energy may be triggered by neutrinos with varying mass (https://arxiv.org/abs/1608.02358). The internship is meant to use a MonteCarlo COSMOMC Boltzmann code to test a simplified framework for this scenario using a prescription we have developed already. The student will be able to use data to test theories, using MCMC simulations.  It will involve collaboration with IAP (F.Fuhrer) and Heidelberg (C.Wetterich), potentially leading to a scientific paper. Availability for 6 months is preferred.

Required skills: python and one language between C or fortran. Previous use of CAMB/CLASS/COSMOMC would be an asset.