Neuromorphic Healthcare Monitoring on FPGA/ASIC

Supervisor: Dr. Somayyeh Timarchi (https://www.qmul.ac.uk/eecs/people/profiles/somayyehtimarchi.html

Project Description

This PhD will develop a spiking neural network (SNN)–based accelerator for real-time, on-device Electrocardiography (ECG) classification in wearable health monitors. The work spans algorithms à architectures à hardware prototyping, targeting continuous cardiac monitoring with energy-efficient learning/inference. Research themes include hybrid training (pretrained layers + lightweight online adaptation), memory-efficient weight/activation handling, and low-power neuron implementations. Prototyping will be on FPGA, with a path to ASIC. This work addresses unmet needs in digital health—personalized, battery-friendly, clinically credible ECG analytics.

PhD topics for this position could explore any combination of the following areas:

- Hardware-Efficient SNN Learning: Develop a hardware-efficient SNN learning mechanism for ECG monitoring. One promising direction is a hybrid training framework that uses pretrained SNN layers for initial inference and lightweight on-device online learning for patient-specific adaptation in ECG monitoring. The goal of this project is to enable on-device training which reduces compute and memory overhead while improving classification accuracy and robustness.

- Memory-Efficient SNN Architecture (with online learning): Design memory organizations that exploit spike sparsity, minimize access frequency, and compress weight/activation storage. The goal is to support online learning by shrinking on-chip memory footprint and bandwidth while maintaining latency/accuracy and supporting integration into compact, wearable form factors for ECG monitoring.

- Low-Power SNN Design: Apply system- and circuit-level low-power techniques to SNN with hardware-friendly neuron models derived from optimized differential equations and approximate arithmetic. Target real-time inference at ultra-low power, preserving throughput and accuracy on FPGA with a path to ASIC.

While I have several ideas related to FPGA/ASIC implementation of Neuromorphic computing, I am also open to supervising a PhD project on adjacent hardware-centric topics such as low-power edge AI for biomedical application, memory- and compute-efficient SNN architectures, spiking neuron design, and hardware-aware learning methods.

Prerequisites:

- A Master’s degree (Distinction or equivalent), or expected completion before starting the PhD,

- Digital design background with VHDL/Verilog and hands-on FPGA experience,

- Solid programming skills (Python, MATLAB, and C/C++),

- Familiarity with signal processing and machine learning, with interest in neuromorphic/SNN methods,

- Motivation to design low-power and real-time hardware for healthcare monitoring

The PhD studentship is funded by EPSRC Doctoral Landscape Award open to those with Home and International fee status. However, the number of students with international fee status who can be recruited is capped according to the EPSRC terms and conditions, so competition for international places is particularly strong. Awards are tenable for up to 3.5 years, and cover tuition fees and a maintenance stipend at the UKRI rate (c. £22,870 p.a. full-time, £11,435 part-time for 2025/26; and 2026/27 rates tbc).

How to apply

Queen Mary is interested in developing the next generation of outstanding researchers and decided to invest in specific research areas.

Applicants should work with their prospective supervisor and submit their application following the instructions at: http://eecs.qmul.ac.uk/phd/how-to-apply/.

The application should include the following:

- CV (max 2 pages)

- Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)

- Research proposal (max 500 words)

- 2 References

- Certificate of English Language (for students whose first language is not English)

- Other Certificates

Please note that in order to qualify as a home student for the purpose of the scholarships, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. For more information please see: (https://epsrc.ukri.org/skills/students/guidance-on-epsrc-studentships/eligibility)

Application Deadline

The deadline for applications is the 28th January 2026.

For general enquiries contact Mrs. Melissa Yeo at m.yeo@qmul.ac.uk (administrative enquiries) or Dr. Arkaitz Zubiaga at a.zubiaga@qmul.ac.uk (academic enquiries) with the subject “EECS 2026 PhD scholarships enquiry”.

For specific enquiries contact Dr. Somayyeh Timarchi at s.timarchi@qmul.ac.uk.