Computer Science: Artificial Intelligence for Paediatric Healthcare: Fully Funded PhD Studentship in digitising healthcare records using Natural Language Processing (NLP) (RS933)

Funding providers: Faculty of Science and Engineering and Cardiff & Vale UHB 

The subject areas: NLP, Machine Learning, Big Data, Paediatric 

The Project start dates: 1 July 2026  **Please see the note below regarding potential later start dates  

Supervisors: Primary supervisor: Dr. Trang Doan, Swansea University, Secondary supervisor: Dr. Deepak Sahoo, Swansea University 

Additional supervisory team members:  
Rhian Thomas-turner, Head of Children's Research Operations and Strategy, Cardiff & Vale UHB 
Angela Strang, Research Development & Trial manager in CYARU, Cardiff & Vale UHB  

Aligned programme of study:  PhD in Computer Science 

Mode of study:  Full-time 

Place of Study: The successful candidate is expected to work closely with the relevant medical staff at Cardiff & Vale UHB. 

Project Description: 
This PhD project aims to transform paediatric healthcare by addressing critical data and research gaps through advanced artificial intelligence (AI). Despite its importance, paediatric healthcare remains under-researched, particularly in areas like rare diseases, neonatal care, and chronic conditions. A major barrier is the lack of AI-ready data due to a lack of electronic health records (EHR) in Wales, meaning most paediatric records are handwritten and unstructured 

The project will prioritise digitising these records using natural language processing (NLP) and machine learning (ML) to create structured datasets. These will support AI applications in paediatric care, enabling early detection of conditions and personalised treatment planning. 

A human-in-the-loop (HITL) approach will ensure clinical expertise guides model development, improving accuracy and trust. Ethical considerations, including data privacy and collaboration with hospital ethics boards, are central to the project. Ultimately, this research seeks to produce AI-ready data creation techniques that will help establish scalable, globally relevant AI solutions and elevate paediatric healthcare to the standards seen in adult medicine. 

The ideal candidate should have background in one of the Machine Learning or Data Science domains with high motivation for upskilling and filling any gaps in their knowledge based on the requirements of the project at the early stages of the study.  

Funding Duration - 4 years

PhD: Applicants for PhD 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). 

English Language 
IELTS 6.5 Overall (with no individual component below 6.0) or Swansea University recognised equivalent. Full details of our English Language policy, including certificate time validity, can be found here. 

Note for international and European applicants: details of how your qualification compares to the published academic entry requirements can be found on our Country Specific Entry Requirements page.  

Please note that the programme requires some applicants to hold ATAS clearance, further details on ATAS scheme eligibility are available on the UK Government website.  

ATAS clearance IS NOT required to be held as part of the scholarship application process. Successful award winners (as appropriate) are provided with details as to how to apply for ATAS clearance in tandem with a scholarship course offer.

This scholarship covers the full cost of tuition fees and an annual stipend at UKRI rate (currently £20,780 for 2025-26) for 4 years. 

Additional research expenses of up to £1,000 per annum will also be available. 

To apply, please complete the entire application form

In order to be considered for this scholarship award the following steps are also required. 

1) In section ‘Programme Related Information’ please input the relevant RS Code for the scholarship award i.e. RS933 

2) In section ‘Research’ you will see ‘Proposed project title/studentship title’* (Mandatory) 

• In ‘Proposed project title/studentship title’ please input both:  

• the RS Code, RS933 and 

• the scholarship title.   

• Please leave Proposed Supervisor field blank 
• Please leave Research Project (if applicable) blank 
• In ‘Do you have a proposal to upload?*’(Mandatory) Please select Yes 
• Then upload copy of advert (you can save the advert by clicking print, and then print to pdf) 

3) In section ‘Funding information’ please choose the option ‘Scholarship Funding’ only. Please ensure no other options are selected.  

*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. 

If you’ve previously applied for this programme, the system will display an “Application Submitted” warning and block a new submission. In this case:  

1. Apply for the same course with the next available start date (e.g., select January if October is unavailable).  
2. Email pgrscholarships@swansea.ac.uk with your student number and the relevant scholarship RS code, requesting the start date be amended to match the advert.  
3. Admissions staff will then update your application accordingly. 

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

NOTE: Applicants for PhD/EngD/ProfD/EdD - to support our commitment to providing an environment free of discrimination and celebrating diversity at Swansea University you are required to complete an Equality, Diversity and Inclusion (EDI) Monitoring Form in addition to your programme application form.    

Please note that completion of the EDI Monitoring Form is mandatory; your application may not progress if this information is not submitted. 

As part of your online application, you MUST upload the following documents (please do not send these via email): 

• 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. 

• One reference (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 

Informal enquiries are welcome; please contact  
Dr. Trang Doan, t.t.doan@swansea.ac.uk  
Dr. Deepak Sahoo, d.r.sahoo@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. 

** In exceptional circumstances, and subject to the discretion of the University and/or the relevant funding body, a deferral of offer may be granted to the next available enrolment period. Such deferral will typically not exceed a duration of three calendar months from the originally stipulated commencement date. Please note that only one deferral may be considered, and any such deferral is not guaranteed.