Computer Science: Fully Funded Swansea University PhD Scholarship: Enhancing Cardiomyocyte Dynamic Network Analysis with Machine Learning (ECIDNA-ML) (RS585)

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)

Supervisors:

• Dr Fabio Caraffini (Fabio.caraffini@swansea.ac.uk)
• Dr Hassan Eshkiki
• Professor Christopher George 

Aligned programme of study: PhD in Computer Science

Mode of study: Full-time

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. 

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 (see country specific qualifications). 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. 

If you have any questions regarding your academic or fee eligibility based on the above, please email pgrscholarships@swansea.ac.uk with the web-link to the scholarship(s) you are interested in. 

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.

To apply, please complete your application online with the following information:

1. Course choice – please select Computer Science / PhD / Full-time / 3 Years / July (or October)

   In the event you have already applied for the above programme previously, the application system may issue a warning notice and prevent application, in this event, please email pgrscholarships@swansea.ac.uk where staff will be happy to assist you in submitting your application.

2. Start year – please select 2024
3. Funding (page 8) –

• ‘Are you funding your studies yourself?’ – please select No
• ‘Name of Individual or organisation providing funds for study’ – please enter ‘RS585 - Machine Learning’

*It is the responsibility of the applicant to list the above information accurately when applying, please note that applications received without the above information listed will not be considered for the scholarship award.

One application is required per individual Swansea University led research scholarship award; applications cannot be considered listing multiple Swansea University led research scholarship awards.

We encourage you to complete the following to support our commitment to providing an environment free of discrimination and celebrating diversity at Swansea University: 

• Equality, Diversity and Inclusion (EDI) Monitoring Form (online form) 

As part of your online application, you MUST upload the following documents (please do not send these via email). We strongly advise you to provide the listed supporting documents by the advertised closing date, where possible:

• CV
• Degree certificates and transcripts (if you are currently studying for a degree, screenshots of your grades to date are sufficient)
• A cover letter including a ‘Supplementary Personal Statement’ to explain why the position particularly matches your skills and experience and how you choose to develop the project.
• Two references (academic or previous employer) on headed paper or using the Swansea University reference form. Please note that we are not able to accept references received citing private email accounts, e.g. Hotmail. Referees should cite their employment email address for verification of reference.
• Evidence of meeting English Language requirement (if applicable).
• Copy of UK resident visa (if applicable)
• Confirmation of EDI form submission (optional) 

Informal enquiries are welcome, please contact Dr Fabio Caraffini (Fabio.caraffini@swansea.ac.uk).

*External Partner Application Data Sharing – Please note that as part of the scholarship application selection process, application data sharing may occur with external partners outside of the University, when joint/co- funding of a scholarship project is applicable.