Details
Anti-ferromagnets are emerging as key materials for a variety of modern technologies across data storage, processing and more. They naturally operate on frequencies much higher than conventional magnets and recent advances have demonstrated how they can be efficiently manipulated on these timescales. At faster timescales the interaction of the atomic structure with the magnetic component is important. Using recently developed models for simulating these components simultaneously this project will explore the dynamics of anti-ferromagnetic materials under strong stimuli and through phase transitions.
This project will explore the complex dynamics occurring in anti-ferromagnetic materials using the framework of atomistic spin dynamics (ASD). At high frequencies the interaction of the atomic magnetic moments with the crystal lattice cannot be ignored and so recent extensions have introduced the coupling between the spins (magnetism) and the crystal lattice.
To undertake this, we will first explore the use of machine learning to create models of the interactions between the spin and lattice in iron rhodium (FeRh) as a prototype anti-ferromagnet. Using these potentials we will model the system under various conditions, such as through the phase transition and under laser excitation. This will create new insights into how these materials can be utilised for novel data storage or processing applications.
Supervisor Bio
Dr Matthew Ellis is a Lecturer within the Department of Computer Science whose research spans both computational physics and machine learning. His research has focussed on the modelling of magnetic materials at the nanoscale and has developed novel models for exploring phenomena such as current-induced dynamics, the spin-lattice interactions and longitudinal fluctuations. His recent research has explored the use of magnetic systems as hardware for machine learning and is interested in the blending of the two topics.
About the Department/Research Group
This role will be based within the Department of Computer Science which has a track record of research excellence; ranking 8th nationally for the quality of our research environment and with 99 percent of our research rated world-leading or internationally excellent. It is vibrant and progressive, supported by a recent multi-million pound investment in the Centre of Machine Intelligence.
The candidate will join the Bio-Inspired Machine Learning Lab, jointly led by Dr Ellis and Prof Eleni Vasilaki, and part of the wider Machine Learning Group. They join a strong interdisciplinary collaboration crossing the Computer Science and Materials Science covering both theoretical and experimental research into using magnetic materials for novel unconventional computing applications.
Candidate Requirements
- Minimum 2.1 Bachelor’s degree or Master’s degree in a relevant discipline (e.g. Physics, Maths, Computer Science or similar), or its international equivalent.
- Experience of neural networks and machine learning, with knowledge of the fundamental methods in this area.
- Be self-motivated with a keen interest in machine learning and/or computational modelling.
- Strong programming skills, ideally Python and C/C++.
- If English is not your first language, you must have an IELTS score of 6.5 overall, with no less than 6.0 in each component.
How to Apply
Please note that this studentship is one of three projects advertised with Dr Matt Ellis and applicants should only apply for one of these studentships. Please indicate which one you are applying for on your application.
To apply for this PhD studentship, applications must be made directly to the University of Sheffield using the Postgraduate Online Application Form. Make sure you name Dr Matt Ellis as your proposed supervisor.
Information on what documents are required and a link to the application form can be found here - https://www.sheffield.ac.uk/postgraduate/phd/apply/applying
The form has comprehensive instructions for you to follow, and pop-up help is available.
You should include a short research statement that:
- outline your reasons for applying for this studentship
- explain how you would approach the research, including details of your skills and experience in the topic area
- be no longer than 3 A4 pages, including references
Funding Notes
The PhD studentship will cover standard UK home tuition fees and provide a tax-free stipend at the standard UK Research Council rate (currently £19,237 for the 2024/25 academic year) for 3.5 years. If you are an overseas student, you are eligible to apply but you must have the means to pay the difference between the UK and overseas tuition fees by securing additional funding or self-funding. Further information on International fees can be found here: https://www.sheffield.ac.uk/new-students/tuition-fees/fees-lookup