PhD Studentship - Machine Learning Digital Twins of Spintronic Neuromorphic Devices

Neuromorphic devices aim to emulate the structure of the human brain to develop resource efficient computing systems. Recent developments have shown strong growth but the complexity of manufacturing many operating elements is challenging to scale indefinitely. To circumvent this, we aim to use bespoke nanoscale devices as ‘complex’ neurons that operate as collections of standard neurons. To train networks of these devices we will use digital twins; machine learning models trained to predict physical systems but are differentiable.

This project will advance the machine learning methods, particularly neural differential equations, for predicting the dynamics of experimental systems designed for neuromorphic computing. A focus will be on how these models can be trained to learn device variations and how they affect performance metrics. It will explore the use of meta-learning to train models so that they can be adapted to new systems with few data points.

Supervisor Bio

Dr Matthew Ellis’ research intersects machine learning and physics; looking to better integrate advances in both to create new paradigms for computing. With a background in theoretical physics, he looks at how unconventional computing systems can be used to create energy efficient hardware for AI applications. He has particular interests in unconventional machine learning algorithms, computational modelling and how studying the brain can inspire new architectures.

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., Computer Science, Physics, etc), or equivalent.
• Self-motivated with experience in machine learning and/or computational modelling.
• Strong programming skills; ideally Python.
• 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.

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