This 3.5-year PhD project is fully funded, and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.
We recommend that you apply early as the advert may be removed before the deadline.
This PhD project will develop nanoscale devices which use magnons (collective excitations in magnetic order) as information carriers for ultra-efficient, compact, brain-inspired computing. This project is positioned at the intersection between computer science and condensed matter physics, offering the student the opportunity to work across both disciplines toward the development of next-generation computing technologies.
The success of neural networks as a platform for developing AI is partially explained by the fact that backpropagation, the primary algorithm used to train them, runs efficiently on conventional CMOS hardware. Yet, the gold standard for intelligence remains biological, and although much is still unknown about the brain, biological learning is believed to rely on fundamentally different, local mechanisms. As the energy and physical resource consumption of AI models continues to scale, there is an increasing need for new computational paradigms that draw on the efficiency and parallelism of biological systems – so-called neuromorphic computing.
Magnonics offers a rich and tuneable platform for wave-based information processing, where interference and nonlinearity can naturally implement neuromorphic functions. This project will investigate their potential through three key research objectives:
- To develop automated design workflows for nanomagnetic devices by combining micromagnetic simulations with machine learning and conventional optimisation techniques.
- To design and optimise magnonic primitives for wave-based neuromorphic computing, including programmable devices enabling nonlinear activation functions and linear multiply–accumulate operations directly in hardware.
- To quantitatively characterise magnonic device performance within neuromorphic computing architectures, evaluating metrics such as energy efficiency, sensitivity to thermal noise, scalability, fabrication feasibility, areal density, and computational throughput.
The successful candidate will join a vibrant and collaborative research environment at the intersection of magnetism, spintronics, and unconventional computing. They will work closely with researchers across physics, materials science, and computer science, gaining experience in both fundamental research and emerging computing technologies. The project offers excellent opportunities to develop advanced computational and modelling skills, contribute to high-impact research, and engage with a broad and international network of collaborators.
Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.
The successful candidate will be capable of performing at a very high level, with motivation to explore and solve open research problems for which solutions are not currently known. They must have good communication, documentation, and time management skills, and must have an enthusiasm for interdisciplinary research, with a willingness to work across the boundaries of physics, materials science, engineering, and computing.
To apply, please contact the main supervisor, Dr William Griggs - william.griggs@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.
