Machine learning for studying supernovae

About the Project

Supernovae are the explosive deaths of certain types of star at the ends of their lives. They play an important role in the Universe, being the key distributors of heavy elements.

With surveys such as the Zwicky Transient Facility and ATLAS, the numbers of these events discovered every year has risen to the 1000s, revealing a whole new variety of supernovae never observed before. In 2025, with the start of science operations with the Vera Rubin Observatory Legacy Survey of Space and Time (LSST), the number of transients will run into the millions every year.

We invite applicants to apply for competitively funded and self-funded positions in our group. The successful applicant will undertake a project considering theory/observations (depending on the applicants' interests) of supernovae, with a view to exploiting new machine learning techniques to cope with the data deluge originating from LSST, other surveys and related simulations. The key goals are to rapidly identify and classify interesting new transients, interpret the outputs of theoretical simulations and conduct high-speed inference. The student will interact with members of Royal Holloway's new Centre for AI.

We welcome applications from all qualified applicants, including international applicants. Applications are particularly encouraged from traditionally under-represented groups in science. The Department of Physics at Royal Holloway holds an Athena SWAN silver award, and the University is an Institute of Physics Project Juno Champion; the award demonstrates action has been taken to address gender equality at all levels and to foster a more inclusive working environment.

Applicants should submit their application using our Applicant Portal. Please note that a research proposal is not required.

Find out more about Research degree (PhD) opportunities at Royal Holloway, University of London.

Applications for PhD degrees are via our Applicant Portal.