Machine Learning for Studying Supernovae Royal Holloway, University of London in United Kingdom

This PhD opportunity at the Department of Physics, Royal Holloway, University of London, focuses on the application of machine learning to the study of supernovae. Supernovae are the explosive deaths of certain types of stars, playing a crucial role in distributing heavy elements throughout the Universe. With the advent of large-scale surveys such as the Zwicky Transient Facility and ATLAS, the number of discovered supernovae has increased dramatically, revealing new varieties and phenomena. The upcoming Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to further increase the number of transient events to millions annually, presenting both challenges and opportunities for data analysis.

The successful applicant will join a research group working at the intersection of theory and observation, with the flexibility to tailor the project to their interests. The core aim is to exploit advanced machine learning techniques to manage and interpret the vast data sets generated by LSST and other surveys, as well as related simulations. Key research goals include rapid identification and classification of new transients, interpretation of theoretical simulation outputs, and high-speed inference. The student will have the opportunity to collaborate with members of Royal Holloway's new Centre for AI, gaining exposure to cutting-edge developments in artificial intelligence as applied to astrophysics.

Royal Holloway's Department of Physics is committed to fostering an inclusive and supportive research environment, holding an Athena SWAN silver award and being recognized as an Institute of Physics Project Juno Champion. Applications are welcomed from all qualified candidates, including international students and those from traditionally under-represented groups in science. The department encourages diversity and has taken concrete steps to address gender equality and inclusivity at all levels.

Applicants should have a strong academic background in physics, astronomy, applied statistics, operational research, or related fields. No research proposal is required for application, and both competitively funded and self-funded positions are available. Funding details are not specified in the current announcement. Applications are accepted year-round via the Royal Holloway Applicant Portal. For more information, prospective students are encouraged to review the PhD opportunities page and contact the department with any specific queries.

This position offers a unique opportunity to contribute to the forefront of astronomical research, leveraging machine learning to unlock new insights into the nature and diversity of supernovae in the Universe.