This project is funded by:
This PhD studentship, in partnership with Northern Ireland Chest Heart and Stroke, represents an exciting opportunity to work alongside an excellent team of researchers across a number of disciplines including Biomedical Sciences, Cardiology, Computational Vision and Bioengineering. This multidisciplinary PhD will allow the successful candidate to gain a wealth of experience across all these areas of research, enriching their portfolio of science, computation and engineering expertise and most importantly develop novel and innovative medical techniques for predicting cardiovascular risk.
Cardiovascular diseases (CVD) especially stroke and coronary artery disease are the world’s greatest causes of mortality and morbidity. The pathological process underlying the condition is subclinical until it develops into an overt disease. Over the years, behavioural, medical, genetic and systemic factors have been used as the main predictors of CVD. These predictors are limited in predicting the disease in a quarter of people who experience CVD.
Various approaches have emerged in an attempt to address the gap. A currently emerging approach is the study of the microcirculation. The first sign of the underlying pathology (atherosclerosis) of the disease is manifest in microvessels. We have developed a non-invasive method of studying the microcirculation by viewing the conjunctival microcirculation at the front of the eye. We use a specially adapted cardiac-gated functional slit lamp biomicroscope and image analysis software to examine the conjunctiva and produce quantified measurements of the microvessels.
The successful candidate will assess extensive data generated from ongoing recruitment of subjects to explore and compare conjunctival microvascular parameters of different study populations of cardiology subjects and control groups.
Aim of the study
The aim of the study is to validate conjunctival microcirculation measurements in predicting cardiovascular health
Objective of the study
The overall objective of the study is to develop a diagnostic algorithm for microvasculature for predictive and prognostic indicators of CVD.
Approaches to the study
The successful candidate will conduct research using the following approaches:
- Computational Vision Analysis of acquired video, image and heart sounds files.
- Use of R-R detection algorithms Machine Learning & Classification of biological signals.
- MATLAB, Signal Processing, Big Data manipulation & analysis.
- Assist with patient recruitment, biomarker assessment and engage with all other team members involved in this multidisciplinary research project.
Please note that candidate must be ordinarily resident in the UK or Islands, including the Channel Islands and Isle of Man, for the full three year period before the first day of the first academic year of the PhD.
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
This project is funded by:
The University offers the following levels of support:
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).
This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Due consideration should be given to financing your studies. Further information on cost of living
Submission deadline
Friday 27 September 2019
12:00AM
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