This project will investigate the convergence of Edge AI and Neuromorphic Computing to enable adaptive, low-power learning systems. Poly-neuromorphic AI refers to a multi-architecture framework combining spiking neural networks and adaptive learning modules. Traditional AI, utilizing deep learning for tasks like medical image analysis are resource-intensive, leading to excessively high energy consumption. This reliance renders them unsuitable for crucial, energy-constrained applications such as wearable medical devices. Neuromorphic computing offers a path for innovation. By processing information using event-driven spiking neurons and synapses, neuromorphic systems enable ultra-low-power, brain-like decision-making. The project aims to create an energy-efficient, dynamically adaptive poly-neuromorphic system for biomonitoring applications, and will contribute to the core disciplines of neuromorphic co-design, biomedical AI, and advanced materials engineering.
The supervisory team for this project area will consist of Prof. Yonghong Peng and Dr Oliver Faust and will also include advisors from Arm Ltd and University of Cambridge. Candidates interested in this project area are encouraged to contact Dr Faust (oliver.faust@aru.ac.uk) for an informal discussion.
Entry criteria
Qualifications: Applicants should have a minimum of a 2.1 Honours degree in a relevant discipline. An IELTS (Academic) score of 6.5 minimum (or equivalent) is essential for candidates for whom English is not their first language.
In addition to satisfying basic entry criteria, the University will look closely at the qualities, skills, and background of each candidate and what they can bring to their chosen research project in order to ensure successful and timely completion.
You will demonstrate excellent knowledge and skills in:
- Electronic Engineering or Computing Engineering
- Advanced Mathematics
- Programming
- Analytics and Problem Solving
