Bayesian Deep Learning for Cosmology with Euclid Royal Holloway, University of London in United Kingdom

This PhD project at Royal Holloway, University of London focuses on the integration of advanced Deep Learning and statistical methods to enable cosmological inference from next-generation astronomical facilities, specifically the European Space Agency’s Euclid mission. The Euclid satellite, launched in July 2023, is set to revolutionize our understanding of the universe by collecting unprecedented data from billions of galaxies. The project aims to address fundamental questions about Dark Energy, gravity on cosmological scales, Dark Matter, and neutrinos.

The successful candidate will join the Euclid Consortium and develop novel methodologies for analyzing the vast Euclid dataset. The scale of this data makes it ideal for applying Deep Learning techniques, and the research will focus on combining these with state-of-the-art statistical inference pipelines to handle the dataset's size and complexity. The candidate will lead the application of these new methodologies to Euclid data, producing high-impact constraints on cosmological models.

Research topics include Geometric Deep Learning, Bayesian inference and hierarchical modelling, simulation-based inference, and differentiable emulation. Applicants should have significant expertise in Python and a strong background in physics, astronomy, computer science, or a related quantitative discipline. The Department of Physics at Royal Holloway is recognized for its commitment to gender equality and inclusivity, holding an Athena SWAN silver award and being an Institute of Physics Project Juno Champion.

Funding for the studentship is subject to confirmation. If awarded, it will cover the UK (Home-fee) rate only. International applicants are welcome but should be aware that any award would cover only the Home-fee component, and additional fees must be covered separately. Applications are encouraged from traditionally under-represented groups in science.

Applications are accepted year-round via the Applicant Portal. No research proposal is required. For more information and to apply, visit the project page.