Machine Learning techniques have revolutionized artificial intelligence. Their application to astrophysics and cosmology permits us to analyze the large quantity of data obtained with current surveys and expected from future surveys with the aim of improving our understanding of the cosmological model.

We will explore machine learning and Bayesian deep machine learning techniques to optimally extract phtometric redshifts of galaxies detected in large-scale surveys. Our primary goals will be to apply our algorithms to simulations of the Vera Rubin Observatory (https://www.lsst.org/about) LLST (Legacy Survey of Space and Time). The Rubin Observatory commissioning activities will start in May 2022, and the PhD candidate will be able to apply her/his algorithms to real data.

This thesis will be hosted by Cosmology group at the Astroparticle and Cosmology (APC) laboratory, in Paris.