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Combining machine learning and physical intuition to understand the properties of black hole mergers


   Cardiff School of Physics and Astronomy

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

About the Project

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 LIGO and Virgo detectors have observed 90 gravitational wave signals from merging black holes and/or neutron stars. In late 2022, they will begin their fourth observing run, with increased sensitivity and the expectation of observing several black hole mergers per week. This drives a need to develop fast and accurate methods for inferring the physical properties of the observed systems. The focus of this PhD project will be to develop machine-learning based methods to perform this rapid parameter estimation. We will incorporate key physical insights, such as how spin-induced orbital precession and higher gravitational wave multipoles impact the waveform, to guide the development of the machine learning algorithms.

The PhD project will also require active involvement in the analysis of data taken during the fourth LIGO-Virgo-KAGRA observing run.

Start date: 1st October 2022

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing provides 4-year, fully funded PhD opportunities across broad research themes:

  • T1: data from large science facilities (particle physics, astronomy, cosmology)
  • T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
  • T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)

 Its partner institutions are Swansea University (lead institution), Aberystwyth University, Bangor University, University of Bristol and Cardiff University.

Training in AI, high-performance computing (HPC) and high-performance data analytics (HPDA) plays an essential role, as does engagement with external partners, which include large international companies, locally based start-ups and SMEs, and government and Research Council partners. Training will be delivered via cohort activities across the partner institutions.

Positions are funded for 4 years, including 6-month placements with the external partners. The CDT will recruit 10 positions in 2022.

The partners include: We Predict, ATOS, DSTL, Mobileum, GCHQ, EDF, Amplyfi, DiRAC, Agxio, STFC, NVIDIA, Oracle, QinetiQ, Intel, IBM, Microsoft, Quantum Foundry, Dwr Cymru, TWI and many more.

More information, and a description of research projects, can be found at the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website. http://cdt-aimlac.org/cdt-research.html

How to apply:

To apply, and for further details please visit the CDT website http://cdt-aimlac.org/cdt-apply.html and follow the instructions to apply online.

This includes an online application for this project at (with a start date of 1st October 2022): https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/physics-and-astronomy)

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

• your academic CV

• a personal statement/covering letter

• two references, at least one of which should be academic

• Your degree certificates and transcripts to date.

In the "Research Proposal" section of your application, please specify the project title and supervisors of this project.

In the funding section, please select that you will not be self funding and write that the source of funding will be “AIMLAC CDT”

The deadline for applications for the UKRI CDT Scholarship in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) is 12th February 2022. However, AIMLAC will continue to accept applications until the positions are filled.

For general enquiries, please contact Rhian Melita Morris [Email Address Removed]

Eligibility:

The typical academic requirement is a minimum of a 2:1 physics and astronomy or a relevant discipline.

Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS) (https://www.cardiff.ac.uk/study/international/english-language-requirements)

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).

For more information on eligibility, please visit the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website http://cdt-aimlac.org/


Funding Notes

The UK Research and Innovation (UKRI) fully-funded scholarships cover the full cost of 4 years tuition fees, a UKRI standard stipend of currently £15,921 per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.

References

GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run, https://arxiv.org/abs/2111.03606
The population of merging compact binaries inferred using gravitational waves through GWTC-3, https://arxiv.org/abs/2111.03634
Measuring gravitational-wave higher-order multipoles https://doi.org/10.1103/PhysRevD.103.024042
Identifying when precession can be measured in gravitational waveforms https://doi.org/10.1103/PhysRevD.103.124023

How good is research at Cardiff University in Physics?


Research output data provided by the Research Excellence Framework (REF)

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