Information-geometric approach to machine learning-based analysis of physiological time series

Eligibility: UK/International (including EU) graduates with the required entry requirements

Funding details: Bursary plus tuition fees (UK/International - including EU at international rates from September 2021)

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: 20 May 2021

Interview date: Will be confirmed to shortlisted candidates

Start date: September 2021

For further details contact: Dr Sergiy Shelyag and Professor Eun-jin Kim

Cotutelle PhD Programme

This studentship is part of the Cotutelle PhD Programme with Deakin University (Australia). Find out more about our global Cotutelle opportunities.

Find out more

Introduction

This fully-funded PhD project is part of the Cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia.

The successful applicant will spend the 1st year at Deakin University and the following year at Coventry University and then the final 1 and a half years at Deakin University.

Project details

The successful candidate will work on development and application of novel information-geometric based machine learning techniques to physiological time series. Humans are complex integrated interconnected multilayer networks of physiological organs, which simultaneously act and continuously interact between each other. A prominent result of such interactions is existence of different semi-stable physiological states, such as wake and sleep. Studying and understanding these interactions and roles of the particular organs in the organisms as a whole, as well as understanding disruptions in communication between different organs, often leading to the whole-network failures, requires development of advanced data science (mathematical, statistical and computational) methods for processing “big data” time-series datasets obtained from direct physiological measurements.

A new, information-geometric theory is a powerful method currently being developed to understand and classify time-varying processes and corresponding time-series data. The key idea is that very nature of time-varying evolution enables us to utilise “geometry" to describe the evolution by quantifying how the “information" unfolds in time through information geometry. The latter refers to the application of the techniques of differential geometry to probability and statistics by using differential geometry to define the metric tensor which endows the statistical space with the notion of length, supporting novel concepts like geodesics and resonances.

In our project, we will develop, apply and test new machine learning algorithms with certain information geometry on the physiological data with the aim to reveal connections, interactions and synchronicity between the different human organs. With mathematical, statistical and data science methods in mind, we will look at a variety of physiological datasets, such as polysomnographic patterns of sleep and sleep fragmentation in healthy people and people suffering from insomnia, or athletic exercises datasets.

Funding

This is a fully-funded studentship, including:

  • Full tuition fees (for up to 4 years)
  • A stipend for up to 3.5 years subject to satisfactory progress
  • A one-time economy return airfare to host institution
  • Conference allowances

Benefits

Throughout the programme, the candidates will have a joint supervisory team and work in close collaboration with researchers in both UK and Australia. The supervisory team offers a substantial track record in successful doctoral supervision and expertise in the thematic areas identified.

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.

Entry requirements

Applicants must meet the admission and scholarship criteria for both Coventry University and Deakin University for entry to the cotutelle programme.

This includes:

  • Applicants should have graduated within the top 15% of their undergraduate cohort. This might include a high 2:1 in a relevant discipline/subject area with a minimum 70% mark (80% for Australian graduates) in the project element or equivalent with a minimum 70% overall module average (80% for Australian graduates).
  • A Masters degree in a relevant subject area, with overall mark at minimum Merit level. In addition, the mark for the Masters dissertation (or equivalent) must be a minimum of 80%. Please note that where a candidate has 70-79% and can provide evidence of research experience to meet equivalency to the minimum first-class honours equivalent (80%+) additional evidence can be submitted and may include independently peer-reviewed publications, research-related awards or prizes and/or professional reports.
  • Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).
  • The potential to engage in innovative research and to complete the PhD within a prescribed period of study.

For an overview of each University’s entry requirements please visit:

Please note that it is essential to be able to physically locate to both Coventry University (England) and Deakin University (Australia).

How to apply

To find out more about the project, please contact Dr Sergiy Shelyag and Professor Eun-jin Kim.

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.

Please note that applications must be made to both universities.

What is the application process?

Applications are submitted to both institutions. Applicants must ensure they meet eligibility requirements. Selection involves academic staff from both institutions. Shortlisted applicants will be interviewed by a panel including academic staff from each institution. Applicants will need to submit copies of certificates to both institutions in line with their respective requirements.

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