About this position
This PhD project focuses on developing innovative healthcare analytics and data-driven approaches to improve patient outcomes, optimise clinical processes, and support sustainable and cost-effective healthcare systems.
The research will explore how clinical data, medical imaging, artificial intelligence (AI), and health economics can be integrated to address complex healthcare challenges and support evidence-based decision-making. A wide range of data sources will be utilised, including clinical records, MRI and other imaging data, radiomics features, public health datasets, wearable technologies, patient-reported outcomes, and economic information.
Using machine learning, statistical modelling, and quantitative analytical techniques, the project aims to develop predictive and decision-support models that enhance patient care, improve service delivery, and support healthcare sustainability. Particular emphasis will be placed on explainable and trustworthy AI methods that are clinically relevant, interpretable, and capable of supporting clinicians, healthcare professionals, managers, and policymakers.
The project builds on recent advances in healthcare analytics, medical imaging, computational modelling, and health economics to investigate complex relationships among clinical, imaging, behavioural, environmental, treatment-related, and economic factors.
The successful candidate will work with real-world datasets, collaborate with healthcare and research partners, publish in international journals, and present at leading conferences.
The research is designed to be flexible and can be tailored to the candidate's background and interests. Potential areas include digital health, sustainable healthcare systems, health economics, healthcare policy, medical imaging, MRI analysis, radiomics, medical physics, clinical decision support, public health, healthcare management, healthcare operations, and AI applications in healthcare. Applicants from medicine, healthcare professions, medical physics, radiography, biomedical sciences, public health, computer science, data science, engineering, mathematics, statistics, and related disciplines are encouraged to apply.
Self Funded (Scholarship not available. Fees & Materials to be paid by the student. Materials costs not significant)
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