Summary
The University of Manchester is offering a fully funded 3.5-year PhD studentship focused on magnon-based spintronic devices for neuromorphic computing. This interdisciplinary project combines condensed matter physics, computer science, and nanotechnology to develop ultra-efficient, brain-inspired computing systems. The research explores how magnons (spin waves in magnetic materials) can be engineered to perform computation with extremely low energy consumption, aiming to support next-generation AI hardware.
PhD Studentship: Magnon Spintronics for Neuromorphic Computing – University of Manchester, UK
Designation
PhD Studentship (Fully Funded)
Key Details
| Parameter | Details |
|---|---|
| University | The University of Manchester |
| Country | United Kingdom |
| Research Duration | 3.5 Years |
| Start Date | October 2026 |
| Funding Type | Fully Funded |
| Stipend | £21,805 per year (UKRI tax-free, 2026/27 rate) |
| Tuition Fees | Fully covered |
| Funding Eligibility | UK (Home) Students |
| Application Status | Open |
Research Area
- Magnon Spintronics
- Neuromorphic Computing
- Condensed Matter Physics
- Nanomagnetic Device Engineering
- Artificial Intelligence Hardware
- Wave-based Information Processing
Location
Manchester, United Kingdom
University of Manchester
Eligibility / Qualification
Applicants should:
- Hold or expect a First Class or Upper Second-Class (2:1) Bachelor’s degree, or a Master’s degree in a relevant field
- Background in physics, materials science, engineering, computer science, or related disciplines
- Strong interest in interdisciplinary research
- Ability to work on open-ended, unsolved research problems
- Good communication, documentation, and time management skills
Job Description / Research Objectives
The PhD researcher will work on developing next-generation magnonic computing systems with focus on:
- Automated Device Design
- Combining micromagnetic simulations, machine learning, and optimization techniques
- Magnonic Neuromorphic Primitives
- Designing wave-based computing elements such as nonlinear activation functions and hardware-level MAC operations
- Performance Evaluation
- Assessing energy efficiency, scalability, noise robustness, fabrication feasibility, and computational throughput
The candidate will work in a collaborative environment spanning physics, materials science, and computing, contributing to advanced research in unconventional computing technologies.
How to Apply
Interested candidates should:
- Contact the main supervisor: Dr. William Griggs
- Email: william.griggs@manchester.ac.uk
- Include:
- Academic background and current study level
- Relevant research experience
- Statement of motivation for this PhD project
- CV and supporting documents (recommended)
Last Date for Apply
2 November 2026
(Early application is strongly recommended as the advert may close before the deadline.)
