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[AIMLAC CDT Studentship] Kinetic Monte Carlo and Machine Learning for Magnetic Monopole Dynamics

   Cardiff School of Physics and Astronomy

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  Dr F Flicker, Prof B Lucini, Dr S Giblin  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.  

Why can't you pull the north pole off a magnet? If you try to cut a magnet in half you get two smaller magnets, each with a north and south pole. Repeat this process and you ultimately end up with a single atom with a magnetic moment, which again has both poles. Essentially, the answer is that while there are particles with electric charge (electric 'monopoles', such as electrons) there are no magnetically charged particles -- magnetic monopoles -- in our universe.

However, there is growing evidence that this is not the whole story [1]. In 1997 a mysterious new type of magnet was discovered, called a spin ice. Our leading theories suggest that the magnetic fields of individual ions in these crystals orient themselves so as to create local sources and sinks of magnetization, which bear a striking resemblance to magnetic monopoles. Recent numerical [2] and experimental work [3] indicates that magnetic monopoles produce unique signatures in the fluctuations of the magnetic field around spin ice crystals, signatures known as 'pink noise' [4].

This project is aimed at understanding how monopoles generate pink noise; how that understanding might be used to definitively prove the monopoles' existence; and how we might put this understanding to use in developing technological applications. Topics will include noise spectra and the detailed study of stochastic signals; modelling and understanding fractals (bond-percolation clusters); loop erased random walks; out-of-equilibrium dynamics; and glassy behaviour.

The work will require the development of advanced numerical methods. We will apply kinetic Monte Carlo (kMC) for modelling all-to-all interactions between large numbers of spins. The project will make heavy use of the HAWK supercomputing facility. Additionally, we will develop a machine learning approach to identifying magnetic monopoles in dynamical measurements, trained using the kMC numerics. The numerical methods are computationally intensive, and will require the development of new methods for running efficiently on the latest heterogeneous high-performance computing architectures (including GPUs and FPGAs). There is scope to undertake analytical modelling to support the numerics, and the project will involve working closely with experimental and numerical groups in Cardiff, Swansea, Oxford, and Cornell.

The project includes substantial external funding for personal development, including public speaking and media training. There is also funding available for 3 months' salary to undertake an industrial placement if desirable. Both are part of the supervisory team's commitment to Responsible Innovation.

Start date: 1st October 2023 

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 2023. 

The partners include: JD Power UK, ATOS, DSTL, Mobileum, GCHQ, EDF, Amplyfi, DiRAC, Agxio, STFC, NVIDIA, Oracle, QinetiQ, 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. 

How to apply: 

To apply, and for further details please visit the CDT website follow the instructions to apply online.  

This includes an online application for this project at (with a start date of 1st October 2023):

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 mid-February 2023. However, AIMLAC will continue to accept applications until the positions are filled. 

For general enquiries, please contact Roz Toft [Email Address Removed]  


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) ( 

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 

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 £17,668per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.


[1] See Dr Flicker's Royal Institution lecture for an overview:
[2] F. K. K. Kirschner, F. Flicker et al., Physical Review B 97, 140402(R) (2018)
[3] R. Dusad, ... F. Flicker et al., Nature 571, 234 (2019)
[4] F. Flicker, Science, accepted for publication

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