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Simulation-based Inference of gravitational waves signals from black holes and neutron stars

Cardiff School of Physics and Astronomy

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Dr V Raymond , Prof Stephen Fairhurst No more applications being accepted Competition Funded PhD Project (Students Worldwide)

About the Project

Black holes and neutron stars are the densest objects in the universe, well beyond what we can produce in a laboratory and at the very edge of our understanding of physics. They lead to puzzling physical consequences, in particular regarding the behaviour of space and time. When they collide, they produce the most violent events in the universe, shaking space and time and creating gravitational waves: ripples in the fabric of spacetime which propagate away at the speed of light. Gravitational-waves were observed for the very first time in September 2015, when two colliding black holes were detected by the LIGO-Virgo collaboration. Since then, several signals have been observed, and we were able to characterise the black holes and neutron stars at the source of those gravitational waves. 

This characterisation currently involves stochastic sampling methods with a very high computational cost, and simplified assumptions of the detectors’ properties. This project will leverage modern advances in likelihood-free inference methods, and in particular simulation-based inference, to solve this inference problem accurately. Our approach will adapt automatically to changing features in the detector noise, allow for new new data to be continuously included, and will be applicable to the upcoming new generation of gravitational-wave detectors. 

Gravitational-wave sources are laboratories where we can measure in neutron stars the equation of state of matter at densities otherwise unattainable, and test General Relativity in the strong field regime. Inference of their extragalactic population enables new understandings of the Universe’s structure of matter, and independent measurements of the Universe’s expansion. 

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced 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.


The typical academic requirement is a minimum of a 2:1 undergraduate degree in biological and health sciences; mathematics and computer science; physics and astronomy or a relevant discipline.  Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it). 

Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS) (

To apply, please visit the CDT website and follow the instructions 

Applicants should apply to the Doctor of Philosophy in Physics and Astronomy with a start date of 1st October 2021.

Applicants should submit an application for postgraduate study via the Cardiff University webpages ( including:

• an upload of your CV

• a personal statement/covering letter

• two references

• Current academic transcripts

In the research proposal section of your application, please specify the project title and supervisors of this project. If you are applying for more than one project, please list the individual titles of the projects in the text box provided. In the funding section, please select ’I will be applying for a scholarship/grant’.

To complete your application please email a pdf(s) of your application to [Email Address Removed]

Funding Notes

The UK Research and Innovation (UKRI) fully-funded scholarships cover the full cost of tuition fees, a UKRI standard stipend of £15,285 per annum and additional funding for training, research and conference expenses.
The scholarships are open to UK/home and international candidates.
For general enquiries, please contact Rhian Melita Morris: [Email Address Removed]
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