Developing magnonic building blocks for neuromorphic computing

About the project

About the Project 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: 1. To develop automated design workflows for nanomagnetic devices by combining micromagnetic simulations with machine learning and conventional optimisation techniques. 2. 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. 3. 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. Eligibility 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. Funding 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. Before you apply We strongly recommend that you contact the supervisor for this project before you apply. 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. How to apply Apply online through our website: https://uom.link/pgr-apply-2425 When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees. Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. After you have applied you will be asked to upload the following supporting documents: Final Transcript and certificates of all awarded university level qualifications Interim Transcript of any university level qualifications in progress CV Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it). Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference) English Language certificate (if applicable) If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk . Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status. We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder). uomcsinformationmanagement | featuredproject8June26 | featuredproject8July26

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