Elsewhere on Ulster
This project is funded by:
Research Context
Positioned within Ulster University’s School of Computing, this research theme focuses on trustworthy and personalised AI for digital health, independent living and healthcare innovation.
Projects investigate passive and unobtrusive sensing for cognitive fatigue, frailty and fall-risk monitoring; vision-language reasoning for context-aware assistance with Activities of Daily Living; and secure healthcare digital twins for personalised medicine and remote patient monitoring.
The research combines multimodal sensing, machine learning, explainable AI, federated learning, synthetic data and privacy-preserving approaches.
Collectively, the projects aim to enable earlier intervention, safer decision support and more personalised care while addressing privacy, reliability, explainability and real-world acceptability.
The projects address priorities in Life & Health Sciences and Software/Cyber and align with the Belfast Region City Deal Centre for Digital Healthcare Technology, supporting preventative, personalised and digitally enabled healthcare and the Programme for Government priorities to cut health waiting times and transform public services.
Passive Sensing and Adaptive Interventions for Managing Cognitive Fatigue in Everyday Contexts
Supervisor Names: Dr George Moore
This PhD research investigates how passive sensing from smartphone, wearables, and other devices can detect acute cognitive fatigue and trigger adaptive interventions in everyday contexts.
Collecting multimodal data, such as Heart Rate Variability, touchscreen interactions, and mobility, the researcher will investigate and develop interpretable machine learning models to infer fatigue in real time and trigger lightweight interventions, such as micro-breaks.
Key outputs will include machine learning models, a sensing pipeline, prototype tools, and empirical findings on user-system interaction.
Applicants should have a background in computing or data science, and academic interests including machine learning, digital health, wearable computing, and programming.
Vision-Language Reasoning for Context-Aware Activity Assistance
Supervisor Names: Dr. Colin Shewell
This project will develop a multimodal AI framework for context-aware recognition and reasoning over Activities of Daily Living (ADLs) within Ambient Assisted Living environments.
Machine vision will extract temporally grounded actions, object interactions and environmental context, which will be transformed into structured activity representations for reasoning by Large Language Models (LLMs) and Vision-Language Models (VLMs).
The research will investigate whether these models can identify and explain temporal, sequential and contextual anomalies, including omitted, repeated, incorrectly ordered and previously unseen behaviours.
The resulting framework will advance conventional activity recognition towards explainable, context-aware ADL reasoning, enabling intelligent systems capable of determining when and why assistance may be required.
AI-Enabled Unobtrusive Sensing for Frailty Monitoring and Fall Prevention
Supervisor Names: Dr. Idongesit Ekerete
Falls and frailty are major challenges associated with ageing, affecting independence, quality of life and health and social care services.
This PhD project will investigate unobtrusive sensing and AI-enabled approaches for monitoring frailty and identifying fall risk in everyday living environments.
The research will explore how sensor data, machine learning and explainable AI can identify digital biomarkers and subtle changes associated with functional decline and emerging health risks.
With emphasis on personalisation, privacy, reliability and real-world acceptability, the project aims to support earlier intervention, preventative healthcare, healthy ageing and independent living, while advancing innovative AI-enabled digital health technologies.
Secure AI for Healthcare Digital Twins
Supervisor Names: Dr. Rashid Kamal
This PhD computing project focuses on developing trustworthy AI frameworks for healthcare "digital twins" to enable personalized medicine and predictive remote patient monitoring.
Recognizing the severe privacy risks of training AI on sensitive medical data, the research proposes a secure framework combining two main techniques.
First, it uses privacy-preserving federated learning to keep patient data decentralized and confidential. Second, it employs secure synthetic data generation to simulate rare clinical cases safely. Finally, the project will evaluate adversarial machine learning techniques to ensure these models can withstand cyber threats, ultimately supporting safer digital health innovation.
Applying to Multiple Projects: Applications for more than one PhD studentship are welcome, however if you apply for more than one PhD project within Computing, 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.
The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education. We particularly welcome female applicants, as they are under represented within the school.
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 to all applicants worldwide, regardless of residency or domicile, and will cover:
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.
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.
Due consideration should be given to financing your studies.
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.
Submission deadline
Friday 8 January 2027
04:00PM
Interview Date
February 2027
Preferred student start date
13 September 2027
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