PhD Studentship - Computational Modelling of Coupled Spin-lattice Dynamics in Anti-ferromagnets

Anti-ferromagnets are emerging as key materials for a variety of modern technologies across data storage, processing and more. Recent advances have demonstrated how they can be efficiently manipulated at high frequency but at this scale the interaction of the magnetisation with the atomic structure is important. This project will explore the complex dynamics occurring in anti-ferromagnetic materials using the framework of atomistic spin dynamics with coupled spin-lattice dynamics. 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 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 applications.

Supervisor Bio

Dr Matthew Ellis’ research intersects machine learning and physics; looking to better integrate advances in both to create new paradigms for computing. 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

The candidate will join the Bio-Inspired Machine Learning Lab, jointly led by Dr Ellis and Prof Eleni Vasilaki. They join a strong interdisciplinary collaboration crossing the Computer Science and Materials Science covering both theoretical and experimental research into spintronic neuromorphic computing. The department has a track record of research excellence; ranking 8th nationally for research environment quality and 99% of our research rated world-leading or internationally excellent.

Candidate Requirements

• Minimum 2.1 Bachelor’s or Master’s degree in a relevant discipline (e.g. Physics, Computer Science, etc), or equivalent.
• Self-motivated with experience in computational modelling and/or machine learning. 
• Strong programming skills; ideally C/C++.
• If English is not your first language: 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. Applicants should only apply for one they are most interested in. Applications must be made directly to the University of Sheffield using the Postgraduate Online Application Form with Dr Matt Ellis named as your proposed supervisor.

You should include a short (up to 3 A4 pages) research statement that outlines your reasons for applying for this studentship and explains how you would approach the research, including details of your skills and experience in the topic area. Information on what documents are required and a link to the application form can be found here: www.sheffield.ac.uk/postgraduate/phd/apply/applying

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 2024/25) for 3.5 years. Overseas students 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 can be found here: www.sheffield.ac.uk/new-students/tuition-fees/fees-lookup