About the Project
Artificial Intelligence (AI) is rapidly transforming our world, but its growing computational demands, particularly for edge computing in devices like IoT sensors, require new, energy-efficient hardware solutions. Over the past decade, memristors, a novel class of electronic components that can "remember" the charge that has flowed through them, have been extensively researched. Their ability to mimic biological synapses and neurons has shown significant potential for revolutionising AI…
applications, particularly in developing energy-efficient neuromorphic (brain-like) computing hardware.
Building on this, our research group has made a recent breakthrough by observing memristor-like behaviour (pinched hysteresis) in micro-electro-mechanical systems (MEMS) resonators for the first time (E. Uka and C. Zhao, 2025). This discovery opens up an entirely new research avenue. We foresee a clear pathway to adopt this mechanical phenomenon for advanced AI systems, enabling cross-domain neural networks, in-sensor AI computation, and neuromorphic sensing. The ultimate goal is to create highly energy-efficient intelligent systems with powerful edge computation capabilities.
This project will explore this nascent and exciting research area. Your primary aim will be to develop a novel approach to realise in-sensor AI computation based on this newly-discovered memristor-like behaviour in micro-mechanical devices. You will delve into several highly topical and interdisciplinary research areas, including MEMS sensors for the Internet of Things (IoT), bio-inspired and unconventional computing, coupled nonlinear microstructures, and machine learning.
You will gain comprehensive, hands-on training and experience in a wide range of tasks essential to the project, such as MEMS sensor design and fabrication, bio-inspired unconventional computing systems, electronic implementation and characterisation. Full-time, tailored training for postgraduate students is provided within our active research groups, ensuring you have the support needed to succeed regardless of your specific degree background. You will have full access to the well-equipped laboratories available at the University of York.
This project will be primarily supervised by Dr Chun Zhao, who has over 10 years of experience conducting research in the field of microsystems (publications include journal papers in Nature Communications), and maintains active collaborations with top universities across the globe (e.g., University of Cambridge, Tohoku University, KU Leuven). The supervisor strongly encourages academic exchanges and innovation. You will join a friendly, supportive, and dynamic research environment with a plethora of opportunities for national and international collaboration.
Academic entry requirements:
Candidates must have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Semiconductor physics, Electronic, Mechanical, and Control Engineering or a closely related subject. Applications will be considered on a competitive basis with regard to the candidate’s qualifications, skills, experience and interests.
How to Apply:
Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process.
This project is open-ended making it suitable for MSc by Research and PhD level.
Funding Notes
This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website View Website for details about funding opportunities at York.