Models of sleep alternation patterns in healthy ageing
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: 2nd June 2021
Interview date: Will be confirmed to shortlisted candidates
Start date: September 2021
For further details contact: Dr Alireza Daneshkhah
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 1.5 years at Deakin University
The supervision team will be drawn from the two Universities.
This project will investigate patterns of sleep and sleep fragmentation related to changes of physiological and cognitive functions as we age. It will consider data from healthy individuals as well as sleep fragmentation which is a result of health or environmental stressors for adults above 40 years of age. The purpose is to investigate the patterns of sleep in different age groups and how these patterns change in healthy individuals and individuals with chronic conditions.
We will use objective methods to measure the effects based on wearable devices and data-driven models (machine learning and mathematical). The project will develop automated methods (machine and deep learning) for sleep classification and sleep stages classification in normal and disturbed sleep in ageing populations.
The project requires a solid mathematics and statistics background and sound programming skills.
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
The Research Centre for Data Science (CDS) aims to develop cutting edge pure research in artificial intelligence, data science and future computing, linking fundamental science to real-world applications.
Data science deals with the analysis and exploitation of large amounts of data (Big Data) drawing together disciplines as diverse as Computer Science, Artificial Intelligence, Statistics and Mathematics. The centre is organised according to three key themes:
- Machine Learning for Big Data
- Wireless Sensors and Internet of Things
- Computational and Statistical Modelling
- Advanced Computing
This project is addressing an important problem investigate patterns of sleep and sleep fragmentation related to changes of physiological and cognitive functions as we age. The project will develop automated methods for sleep classification and sleep stages classification in normal and disturbed sleep in ageing populations. The project requires a solid mathematics and statistics background and sound programming skills.
On the other hand, employing such cutting-edge ML techniques, strengthens the candidate skills and support the candidate for future career.
In addition, 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.
Applicants must meet the admission and scholarship criteria for both Coventry University and Deakin University for entry to the cotutelle programme.
- 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).
- This PhD is suitable for candidates with background in data analysis, mathematics, computer science and relevant discipline.
- A good knowledge of mathematical modelling, machine learning and AI concepts, linear/non-linear systems, and willingness to quickly learn further in this area.
- Digital sensors signal and data acquisition is an advantage but is not mandatory.
- Good programming skills in MATLAB/Python.
- Good writing and interpersonal/communication skills.
How to apply
To find out more about the project, please contact Dr Alireza Daneshkhah.
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.