Elsewhere on Ulster
This project is funded by:
This PhD will develop and evaluate clinician-led artificial intelligence tools to support safer, more personalised management of cardiometabolic multimorbidity, including type 2 diabetes, cardiovascular disease, hypertension and chronic kidney disease.
Working across Ulster University and collaborating hospitals in China, the researcher will use routinely collected, de-identified clinical data to develop risk-stratification models for clinically important adverse events, with initial exemplar outcomes including acute kidney injury and severe hypoglycaemia.
The project will move beyond model accuracy alone. The researcher will assess calibration, clinical utility, generalisability across healthcare settings, subgroup performance and explainability, and will work with clinicians to design outputs that can be integrated into real-world decision making.
A staged programme will include data harmonisation and clinically informed feature development; model development and external validation across centres; and clinician-in-the-loop evaluation of usability, acceptability and implementation requirements. The project will also examine governance, ethical and regulatory considerations relevant to safe use of health AI and its future translation to Northern Ireland healthcare.
The successful candidate will join an interdisciplinary international team and receive training in health data science, machine learning, clinical research, responsible AI, data governance and implementation science. The intended outputs are validated predictive models, prototype modular decision-support tools and a transferable implementation framework for safe clinical AI in complex chronic disease care.
Please note: Research Proposal - 5 pages in total
The proposal must include:
Please also note that the use of AI in generating the research proposal is prohibited.
Important Information: Applications for more than one PhD studentship are welcome, however if you apply for more than one PhD project within Biomedical Sciences/Pharmacy and Pharmaceutical Sciences, your first application on the system will be deemed your first-choice preference and further applications will be ordered based on the sequential time of submission.
If you are successfully shortlisted, you will be interviewed only on your first-choice application and ranked accordingly. Those ranked highest will be offered a PhD studentship.
In the situation where you are ranked highly and your first-choice project is already allocated to someone who was ranked higher than you, you may be offered your 2nd or 3rd choice project depending on the availability of this project.
Please note, the successful candidate will be required to obtain AccessNI clearance prior to registration due to the nature of the project.
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.
The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.
Appointment will be made on merit.
This project is funded by:
PhD Scholarships are available for this project and will cover:
To be eligible for these scholarships applicants must: be a UK or Irish national; or have pre-settled or settled status; or have indefinite leave to remain or enter.
Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme i.e. September 2027.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year, or part-time equivalent, funded from any source, including self-financing students, are NOT eligible to apply for an award.
Students in full-time employment are not eligible for an award.
Due consideration should be given to financing your studies.
Part-time PhD scholarships may be available at a 0.5 pro rata rate of the full-time scholarship, which would require a six-year registration period.
The University will award a successful candidate one of the following scholarships for the project, depending on the funding available at the time an admissions offer is made:
Please note that the Terms and Conditions for scholarships are subject to change for 2027/28.
Collins GS, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378.
Vasey B, et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nature Medicine. 2022;28:924–933.
Lekadir K, et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ. 2025;388:e081554.
World Health Organization. Ethics and governance of artificial intelligence for health. Geneva: WHO; 2021.
Department for the Economy. Life and Health Sciences Sectoral Action Plan. Northern Ireland; 2025.
Submission deadline
Friday 8 January 2027
04:00PM
Interview Date
between 8 -16 February 2027
Preferred student start date
13 September 2027
Telephone
Contact by phone
Email
Contact by email