Funding provider: UK Research and Innovation (UKRI)
Subject areas: Physics (and Mathematics)
Project start date: 1 October 2023 (Enrolment open from mid-September)
Aligned programme of study: PhD in Physics
Mode of study: Full-time only
Project supervisors:
- Professor Ivonne Zaval
- Professor Biagio Lucini
Project description:
Artificial Intelligence, Machine Learning and Advance Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.
Research theme: T1 - data from large science facilities and T3 - novel mathematical, physical, and computer science approaches
The recent discovery of gravitational waves from binary black holes and neutron stars has open up new possibilities to solve some of the greatest mysteries of the universe: the nature dark energy, dark matter, and gravity at the highest scales.
The project's research involves both theoretical and computational development aimed at developing tests of the dark sector, DM & DE, using gravitational waves, focusing on the science of the Einstein Telescope (ET). In particular, the project will develop tests of the dark sector in these theories - namely dark matter and dark energy - using gravitational waves. It will use and develop analytical techniques, as well as numerical methods, combined with suitable computational tools, to 1) test dark matter candidates, such as primordial black holes, ultralight scalars or vector fields, as well as possible dark matter particles accreting on compact objects; 2) test the nature of dark energy and possible modifications of GR at cosmological distances, using gravitational wave observations.
It is expected that the student will join the ET collaboration as a member of the Swansea Research Unit, as well as the Swansea-Liverpool node of the European Consortium for Astroparticle Theory (EuCAPT).
More information can be found at the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing (AIMLAC) website.
Please quote the project code (e.g. RS193 - AIMLAC3) for queries and within the application.
Eligibility
Applicants for PhD must normally hold an undergraduate degree at 2.1 level or a master’s degree with a minimum overall grade at ‘Merit’ (or Non-UK equivalent as defined by Swansea University).
English Language requirements: If applicable – IELTS 6.5 Overall (with no individual component below 6.0) or Swansea University recognised equivalent.
This scholarship is open to candidates of any nationality.
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