Machine learning assisted optimization techniques for fitting excitonic spin-orbit models to big data

A PhD studentship is available in the group of Chris Stock (School of Physics and Astronomy, The University of Edinburgh in collaboration with Russell Ewings (STFC-ISIS) and Bo Liu (University of Glasgow School of Electrical Engineering). The studentship is fully funded for 48 months by the University of Edinburgh and the Ada Lovelace Centre (STFC) and covers tuition fees and an annual stipend (starting at £19,237 per annum) for a candidate satisfying EPSRC residency criteria. https://www.ukri.org/councils/esrc/career-and-skills-development/funding-for-postgraduate-training/eligibility-for-studentship-funding/#contents-list

Please apply for this studentship using our Degree Finder and include the scholarship title as the thesis title and Dr Stock as the proposed supervisor. https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&edition=2024&id=190  

Project Summary

This PhD project is to develop and use machine learning-assisted global optimization techniques to find ways of performing high-quality fits of large neutron scattering datasets to excitonic models of quantum magnets. The research focus is machine learning algorithms, optimization algorithms and the hybrid of them for the targeted real-world scientific application. The application area involves a key topic in modern condensed matter physics, the search for and utilization of a quantum spin liquid. The insights gained into such materials are informing new approaches to making quantum computers that are more fault tolerant.  

As well as the computational work, the student will be involved with conducting the neutron scattering experiments at central facilities in the UK, Europe, and North America.

Suitable candidates are those with a solid computer science, mathematics, or computational physics background, and hopes to carry out in-depth research in this area. Although a science background is not required, the student must have a strong interest in scientific research and aspirations to apply the former to the latter.

Qualifications and requirements

 • A degree in computer science, mathematics, physics, engineering, or related fields, with a 1st class or at least upper-second class honours or equivalent

• An excellent knowledge and skills with computing and experience with programming language(s) such as Matlab, Python, C++, etc.

• Competence in machine learning and/or optimization.

• An interest in current topics in magnetism and quantum materials.

• A willingness to travel overseas to conduct experiments at central facilities.

The position will remain open until filled