Neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), affect tens of millions of people worldwide. Although the causes of AD and PD are still not fully understood, early diagnosis and an accurate characterization of the disease progression can be very important for the treatment and the improvement of the patients’ life quality. Currently, the diagnosis of neurodegenerative diseases mainly relies on mental status examinations and neuroimaging scans, which are expensive, time-consuming and sometimes inaccurate.

New cost-effective and accurate diagnosis tools and techniques are therefore urgently needed especially for the early detection and prediction of AD and PD at the individual level. Over the last decade, electroencephalography (EEG) has emerged as an economical and non-invasive alternative technique for the study of neurodegenerative diseases. It is well-known that AD and PD patients are characterised by a reduced complexity of cortical activity and a slowing of oscillatory brain activity, therefore, it is important to study how neural activity is coordinated across different spatial and temporal scales for the diagnostic purpose. In this PhD project, we will investigate how brain connectivity analysis, network theory, and advanced deep learning approaches (e.g. RNN, GNN, transformers) can be integrated to develop new EEG-based biomarkers for the early diagnosis of neurodegenerative diseases.

This project will be based on the existing work from both UK and Singapore supervisors’ groups on computational neuroscience, nonlinear signal processing, and deep learning.

PLEASE NOTE: This is a 4-year collaborative studentship which requires the candidate to spend two full years based at Coventry University (UK) and two years based at an A*Star Research Institute (Singapore). The usual pattern is first and fourth years at Coventry and second and third year at an A*Star Research Institute.

Coventry University and A*Star will only cover the stipend up to a maximum of two years each. Changes to the mobility pattern will only be considered under exceptional circumstances and can impact on the duration of the course and level of funding available. Should a candidate request any changes to mobility which results in the period spent in either the UK or Singapore extending beyond two years then the candidate is responsible for covering the stipend for that period.

Funding

Full tuition fees, bursary (£18,622 p/a), and additional allowances.

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 College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.

Candidate specification

A minimum of a 2:1 first degree in Mathematics/Statistics, Computer Science, Engineering, Computational Biology/Neuroscience with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
Competent programming skills (in Matlab, Python, R or Julia) and experienced in mathematics/statistics and numerical analysis;
Interest in machine learning, mathematical modelling or statistical inference, and enthusiastic to work on an inter-disciplinary research project.
PLUS
the potential to engage in innovative research and to complete the PhD within 4 years
a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component), if you are an EU (non UK) or overseas national.

For further details please visit: https://www.coventry.ac.uk/research/research-opportunities/research-students/making-an-application/research-entry-criteria/