About the Project
Artificial Intelligence (AI) has revolutionized our daily lives in various domains with spectacular applications; however, it poses emerging challenges due to the ever-growing demands of performance and energy efficiency. Meanwhile, Moore's law is coming to an end due to the physical limits of technology scaling, taking into consideration that AI models are being scaled by orders of magnitude higher than Moore's Law. These challenges become more significant for edge AI applications (such as…
bio-electronic systems for health care or monitoring and surveillance for security & defense); because of the resource-constrained environment where only limited hardware budgets (Energy/Power/Area) are available.
In this project we propose building reconfigurable computing architectures that maximize not only the hardware specialization and computing parallelism for a higher energy efficiency but also the data utilization to mitigate the memory wall's penalty. These reconfigurable architectures will be adaptable to the runtime AI workloads, by reconfiguring the hardware with the optimum architectural setup through different combinations of bit-precisions, data representations and micro-architectural organizations; thereby enabling the highest energy-efficient execution per each phase of the AI workload, unlike conventional fixed accelerators which are optimized holistically for a few workloads.
This project is expected to start in September 2027.
Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. Dr. Shady Agwa, email: shady.agwa@manchester.ac.uk
How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships. If you don't already have an applicant account, please follow the instructions here.
When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications. Please note late or incomplete applications will not be considered.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status.
We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder.
Eligibility: The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in Electronic Engineering (or international equivalent) OR any upper-second class (2:1) honours degree and a Master's degree at merit in Electronic Engineering (or international equivalent).
This project will remain open until filled.
If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026.
If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027.
Self or externally funded students can also be considered for this project.
FSE_SoE
Funding Notes
Funding for this project covers tuition fees, UKRI minimum annual stipend (currently £21,805/annum) and up to a £2.5k/annum research training support grant for the full duration of the 3.5-year programme.