Graph deep learning for neurodegenerative disease detection
Eligibility: UK/International (including EU) graduates with the required entry requirements
Duration: Full-Time – between three and three and a half years fixed term
Application deadline: 30 September 2025
Interview date: Will be confirmed to shortlisted candidates
Start date: January 2026
For further details contact: Dr Fei He
Introduction
Neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), affect tens of millions of people worldwide and cause a heavy economic burden on societies. Early diagnosis and an accurate characterization of disease progression are critical for treatment and improving patients' quality of life. However, current methods rely on expensive and time-consuming mental status examinations and neuroimaging scans, which are often inaccurate. There is an urgent need for cost-effective and accurate diagnostic tools for early detection of neurodegenerative diseases.
Project details
In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases. Electroencephalography (EEG) has emerged as a non-invasive and economical alternative technique for studying neurological disorders. However, current approaches rely primarily on single-channel or pairwise connectivity analysis, focused on selected electrodes or brain regions. We would like to investigate how graph deep learning models can be designed to capture dynamics in brain signals for the accurate detection, and how the interpretability of these models can be enhanced to support clinical decision-making. This project will leverage the complementary expertise of both supervisory teams in EEG signal processing, graph deep learning, dynamical systems and statistical physics. The candidate will be jointly supervised by the Coventry team Dr Fei He and the Stellenbosch team Prof. Francesco Petruccione. This project will contribute to the new theoretical development of graph deep learning, and contribute to the research community of brain connectivity and network neuroscience.
This is a joint Phd studentship between Coventry (UK) and Stellenbosch (South Africa) you will registered for a PhD at both Universities. The successful candidate will be based at Coventry University and will spend a few months at Stellenbosch (South Africa)
This project will leverage the complementary expertise of both supervisory teams in EEG signal processing and statistical physics. You will be jointly supervised by the Coventry team Dr Fei He and the Stellenbosch team Prof. Francesco Petruccione
Funding
Tuition fees and bursary
Benefits
The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral and Researcher College, which provides support with high-quality training and career development activities.
Entry requirements
- A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
- A Masters’s degree with a minimum mark of 60% in the dissertation
PLUS
- The potential to engage in innovative research and to complete the PhD within 3.5 years.
- A minimum of English language proficiency (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component).
How to apply
In the first instance please submit your expression of interest via the button below with a supporting statement detailing your suitability with evidence of the following:
• Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics.
• Knowledgeable in machine learning techniques (had successful courses or projects)
• Be proficient in programming (preferably in Python ot Matlab).
• Ideally familiar with machine/deep learning, signal processing, dynamical system or mathematical modelling
To find out more about the about the technical details of the project, please contact: Dr Fei He
Apply to Coventry University
