Fully Funded Swansea University PhD Scholarship: Enhancing Cardiomyocyte Dynamic Network Analysis with Machine Learning
Found in: beBee S GB - 2 weeks ago
Funding providers: Swansea University's Faculty of Science and Engineering
Subject areas: Computer Science (Machine Learning/Pattern Recognition applied to molecular cardiology)
Project start date:
- 1 July 2024 (Enrolment open from mid-June)
- 1 October 2024 (Enrolment open from mid-September)
Project description:
This project represents a new approach to map dynamical interactions in networks of human cardiac cells. Network dyssynchronisation is a fundamental event in the catastrophic breakdown of heart rhythm but we do not know the causative events that lead to the failure of cell-to-cell interactions. Moving beyond vague observational descriptions of network behaviours this project will implement a new system to precisely quantify time-resolved information on cell-to-cell interactions. Our approach involves the development of an innovative methodological framework that employs machine learning (ML) to define the intricate nature of intercellular interactions in such networks. We will utilise large datasets and videos acquired from human cellular networks under a range of experimental conditions designed to stabilise or destabilise functional coupling between cells in the networks. The project will use our expertise in developing tailored algorithms for information extraction, pattern recognition and uncertainty estimation concerning the available clinical data. ML algorithms will enable new predictions and signal extrapolation from image datasets of cellular network behaviour. This new framework will add new knowledge on the spatial and temporal nature of intercellular dyssynchronisation and yield unprecedented insights into cardiomyocyte network dynamics. The outputs of this work will lead to an improved understanding of the early events underpinning the functional decline of heart muscle and will ultimately inform better diagnosis and therapeutic interventions in heart disease.
Eligibility
Candidates must hold an undergraduate degree at 2.1 level in Computer Science, Mathematics or a closely related discipline, or an appropriate master’s degree with a minimum overall grade at ‘Merit’ (or Non-UK equivalent as defined by Swansea University). If you are eligible to apply for the scholarship (i.e. a student who is eligible to pay the UK rate of tuition fees) but do not hold a UK degree, you can check our comparison entry requirements. Please note that you may need to provide evidence of your English Language proficiency.
Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA regulations.
Additional Funding Information
This scholarship covers the full cost of UK tuition fees and an annual stipend at UKRI rate (currently £18,622 for 2023/24).
Additional research expenses will also be available.
£18,622 p.a.
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Fully Funded Swansea University PhD Scholarship: Enhancing Cardiomyocyte Dynamic Network Analysis with Machine Learning
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