Evaluating clinical AI in real-time

This PhD project forms part of the National Infrastructure for Real-time Clinical AI Trials (NASCENT), which is aimed at establishing Australia’s first research infrastructure to support the evaluation of clinical artificial intelligence (AI) technologies in healthcare settings.

NASCENT seeks to develop a secure and scalable research environment in which clinical AI technologies can be evaluated using real-world healthcare data before and during implementation. The NASCENT infrastructure is intended to support the assessment of AI systems across their lifecycle, from initial validation through to prospective evaluation, implementation and ongoing performance monitoring.

You will investigate methods for evaluating clinical AI in real-world healthcare environments. The final research focus will be developed with the supervisory team and may address areas such as:

• monitoring changes in data distributions and model performance over time, including data and model drift;
• evaluating the fairness and performance of AI systems across different patient populations;
• developing approaches for post-deployment monitoring and ongoing validation of clinical AI;
• evaluating the safety, reliability and generalisability of AI models across healthcare settings;
• developing methods or frameworks for responsible AI lifecycle management and governance; or
• investigating emerging approaches to the evaluation of clinical AI technologies.

You will have access to the NASCENT research infrastructure and opportunities to work with multidisciplinary teams across clinical practice, data science, digital health and health service research.

The project will initially involve reviewing current methods and identifying an area in which new methodological or empirical research could improve the evaluation of clinical AI. You will then develop and test their proposed methods using clinical AI applications and datasets available through NASCENT, with opportunities to evaluate these approaches within real-world healthcare environments.

The project is suitable for candidates interested in the intersection of artificial intelligence, data science and healthcare, particularly the challenges involved in translating AI systems from development into safe and effective clinical use.

This is an Fellowship support scheme scholarship project that aligns with a recently awarded Australian Government grant.

The scholarship includes:

• living stipend of $39,220 per annum tax free (2026 rate), indexed annually
• your tuition fees covered
• Top-up of $10,000 per annum for three years

Learn more about the Fellowship support scheme scholarship.

Your application will be assessed on a competitive basis, taking into account your previous academic record, research experience, publication record, honours and awards, and relevant employment experience.

Applicants should demonstrate academic achievement and research potential in one or more relevant fields, such as:

• computer science or software engineering;
• artificial intelligence or machine learning;
• data science, statistics or biostatistics;
• biomedical or health informatics;
• digital health;
• epidemiology or health services research; or
• another quantitative health or computing-related discipline.

Experience with quantitative data analysis and programming, particularly using Python or similar languages, would be advantageous. Experience in machine learning, statistical modelling, data engineering, clinical data analysis, or AI evaluation would also be beneficial.

Applicants with a healthcare or clinical background are encouraged to apply where they can demonstrate strong quantitative, computational, or health data research skills.

Knowledge or experience in areas such as responsible AI, algorithm fairness, model monitoring, implementation science, clinical informatics, or healthcare AI governance would be highly regarded but is not essential.

This project requires candidates to commence no later than Research Quarter 2, 2027. You can start in an earlier research quarter.

You must submit an expression of interest (EOI) by the closing date for the research quarter (RQ) you want to start in:

Before you apply

1. Check your eligibility for the Doctor of Philosophy (PhD).
2. Prepare your documentation.
3. If you have any questions about whether the project is suitable for your research interests, contact Professor Clair Sullivan (c.sullivan1@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the 'Scholarship/Sponsorship' section with the following details:

1. Are you applying for an advertised project: 'Yes'
2. Project: 'Fellowship project scholarship'
3. Scholarship Code Listed in the Advertisement: AI-SULLIVAN
4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/evaluating-clinical-ai-real-time

Submit an EOI