Enhancing AI Medical Applications on AMD Cutting-Edge AI Hardware
Director of Studies (DoS): Dr. Vasilios Kelefouras (v.kelefouras@plymouth.ac.uk )
2nd Supervisor: Dr. Vivek Singh (vivek.singh@plymouth.ac.uk)
3rd Supervisor: Prof. Shangming Zhou (shangming.zhou@plymouth.ac.uk )
Industrial Partner: This project is partly funded by Advanced Micro Devices (AMD) . AMD is a leading global semiconductor manufacturer and technology company.
Applications are invited for a 3.5-year EPSRC funded UDLA PhD studentship. The studentship will start on 1st October 2026.
Project Description
As Deep Learning and Generative AI applications continue to expand, optimizing computational efficiency is becoming increasingly critical, particularly for AI in resource-constrained environments or at the edge.
To address this challenge, semiconductor manufacturers have introduced dedicated Neural Processing Units (NPUs), significantly enhancing performance and energy efficiency. AMD, for example, integrates a CPU, NPU, and GPU into its latest Ryzen processors, unlocking new possibilities for on-device AI acceleration.
This project aims to maximise the potential of AMD’s cutting-edge hardware for healthcare computer vision applications. The project is partially funded by AMD, and the successful candidate will collaborate with AMD researchers.
As part of this research, you will:
- Investigate how different AI tasks perform on AMD Ryzen CPU, GPU, and NPU, in terms of inference speed, energy consumption, accuracy, and performance per watt. Different quantization levels (e.g., int8, fp16) will also be explored.
- Develop intelligent workload allocation techniques to maximise resource utilisation across heterogeneous hardware.
- Tailor Vision Language Model architectures for efficient deployment on AMD NPUs.
- Validate these methods on selected real-world healthcare applications.
Why Apply?
- Conduct high-impact research in AI system acceleration.
- Collaborate with AMD engineers, gaining valuable industry experience.
- Work on cutting-edge hardware and emerging AI models, including Vision Language Models.
- Benefit from strong career prospects in both academia and industry.
Who We Are Looking For
We seek highly motivated candidates with expertise in programming (C++, Python), computer architectures and Deep Learning (PyTorch, TensorFlow).
Exceptional international candidates may be eligible for a fee waiver (read the following section carefully).
Eligibility
Applicants should have a first or upper second class honours degree in an appropriate subject and preferably a relevant Masters qualification. Applications from both UK and overseas students are welcome.
The studentship is supported for 3.5 years and includes full Home tuition fees, Bench fee plus a Stipend of £21,805 per annum 2026/27 rate. The studentship will only fully fund those applicants who are eligible for Home fees with relevant qualifications. Applicants normally required to cover International fees will have to cover the difference between the Home and the International tuition fee rates. The international component of the fee may be waived for outstanding international applicants.
There is no additional funding available to cover NHS Immigration Health Surcharge (IHS) costs, visa costs, flights etc.
- The studentship is supported for 3.5 years of the four-year registration period. The subsequent 6 months of registration is a self-funded ‘writing-up’ period.
- You can’t work full time while receiving a PhD stipend.
If you wish to discuss this project further informally, please contact Dr. Vasilios Kelefouras v.kelefouras@plymouth.ac.uk .
To apply for this position please click on the Apply button above.
For more information on the admissions process generally, please contact research.degree.admissions@plymouth.ac.uk
The closing date for applications is 12 noon on 24 April 2026. Shortlisted candidates will be invited for interview shortly thereafter.