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Network inference and machine learning: understanding brain connectivity and neurological disorders


Faculty of Engineering, Environment & Computing

, Dr Min Wu Monday, April 26, 2021 Funded PhD Project (Students Worldwide)
Coventry United Kingdom Applied Mathematics Data Analysis Neuroscience Mathematics Software Engineering Statistics

About the Project

Coventry University is inviting applications from suitably qualified graduates for a fully-funded PhD studentship. The successful candidate will join the project ‘Network inference and machine learning: understanding brain connectivity and neurological disorders’ led by Senior Lecturer Dr Fei He (Machine Learning and Computational Neuroscience) at Coventry University, and Dr Min Wu, at the A*STAR Institute for Infocomm Research (I2R) in Singapore.

The Centre for Data Science is a recently established research centre with a vision to become an internationally recognised research centre in the field of Artificial Intelligence and Data Science. Currently, the research themes of the centre include Machine Learning, Computational Biology and Neuroscience, Statistical and Mathematical Modelling, Wireless Sensors and the Internet-of-Things.

This research project is a collaboration between Coventry University and Singapore’s Agency for Science, Technology and Research (A*STAR). The successful candidate will have the opportunity to conduct their research project both at Coventry University, UK and for up to 2 years at the A*STAR Institute for Infocomm Research (I2R) in Singapore.

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 advanced brain connectivity analysis, network inference, and machine/deep learning approaches can be used 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, network inference and machine learning.

Other Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills at the Doctoral College and Centre for Research Capability and Development at Coventry University

Entry Requirements

  • A minimum of a 2:1 undergraduate degree in Mathematics/Statistics, Computer Science, Engineering, Computational Biology/Neuroscience or a related discipline with a minimum 60% mark in the project
  • In the event of a undergraduate degree classification of less than 2:1, a Master’s Degree in a relevant subject area as mentioned above will be considered as an equivalent;
  • The Masters must have been attained with minimum overall marks at merit level (60%). In addition, the dissertation or equivalent element in the Masters must also have been attained with a minimum mark of merit level (60%);
  • 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;
  • Minimum English language proficiency of IELTS Academic 7.0 with a minimum of 6.5 in each component, if you are an EU (non UK) or overseas national;

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project. See proposal guidance at https://www.coventry.ac.uk/globalassets/media/global/writing-your-proposal.pdf

For further details see: https://www.coventry.ac.uk/research/research-students/making-an-application/

How to apply

To find out more about the project please contact Dr Fei He at

To apply online please visit: https://pgrplus.coventry.ac.uk/ 

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project. 

Key dates

Duration of study: Full-Time – for a maximum of four years duration

Interview dates: Will be confirmed to shortlisted candidates

Start date: Sept 2021


Funding Notes

Coventry University and A*STAR jointly offer a fully-funded PhD studentship that is open to both UK/EU and international graduates as part of the A*STAR Research Attachment Programme (ARAP).
The studentship is fully funded and will include:
• full tuition fees
• a stipend for up to 4 years (£15285 approx) subject to satisfactory progress
• a one-time airfare to and from Singapore
• a one-time settling-in allowance in Singapore
• medical insurance for the period in Singapore
• Conference allowances.

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