PhD Studentship: Magnon Spintronics for Neuromorphic Computing

Overview

This fully funded 3.5-year PhD project at The University of Manchester will develop nanoscale devices that use magnons, which are collective excitations in magnetic order, as information carriers for compact, ultra-efficient, brain-inspired computing. It sits between computer science and condensed matter physics.

The success of neural networks is partly due to backpropagation running efficiently on conventional CMOS hardware, yet biological learning is believed to rely on different, local mechanisms. As AI consumes ever more energy and resources, neuromorphic computing that borrows the efficiency and parallelism of biological systems is attractive. Magnonics offers a tuneable platform for wave-based information processing, where interference and nonlinearity can implement neuromorphic functions. The project has three objectives. The first is automated design workflows for nanomagnetic devices combining micromagnetic simulation with machine learning and conventional optimisation. The second is designing and optimising magnonic building blocks for wave-based neuromorphic computing, including programmable devices for nonlinear activation functions and linear multiply-accumulate operations in hardware. The third is quantitatively assessing device performance within neuromorphic architectures, covering energy efficiency, sensitivity to thermal noise, scalability, fabrication feasibility, areal density and computational throughput.

Who can apply

Home students are eligible to apply. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master's, or an international equivalent, in a relevant science or engineering discipline. The candidate should be capable of working at a very high level, be motivated to tackle open problems, have good communication, documentation and time management skills, and be enthusiastic about interdisciplinary work across physics, materials science, engineering and computing.

What you get

The successful candidate receives an annual tax-free stipend at the UKRI rate, which is 21,805 pounds for 2026/27 and is expected to increase each year, and tuition fees are paid. The student joins a collaborative environment spanning magnetism, spintronics and unconventional computing.

How to apply

Applicants contact the main supervisor, Dr William Griggs, with their current level of study, academic background, relevant experience and a paragraph on their motivation. Early application is recommended because the advert may be removed before the deadline.

Key dates

The closing date is 2 November 2026, and the start date is listed as October 2026.