PhD Top-Up Scholarships
The Centre for Big Data Research in Health (CBDRH) is excited to launch Top-Up Scholarships for high-achieving domestic and international candidates seeking to start a PhD in 2026.
About CBDRH
Established in 2014 at the аIJʹÙÍø Faculty of Medicine and Health, CBDRH is Australia's first research centre dedicated to health research utilising large-scale electronic data across biomedical, clinical, health services, and public health domains. The Centre engages with government, industry, healthcare providers, communities and consumers through codesign and coproduction methods to ensure the effective translation and implementation of research findings into health policy, services, and programmes.
About the Scholarships
Expressions of interest (EOI) are invited from potential candidates who are seeking to undertake a nominated research project in the Centre. Top-up scholarships of $10,000 per annum will be available for a period of 3 years, with the possibility of a 6-month extension depending on progress.
It is expected that candidates will apply for a full scholarship in the September аIJʹÙÍø Scholarships round, to commence study in 2026. More Information about scholarships can be found at Postgraduate Research Scholarships.
All potential candidates must meet the eligibility requirements for entry to a research higher degree program at аIJʹÙÍø, including the English language, qualifications and residency requirements.
See below for full details on the research projects eligible for a top-up scholarship. Top-up scholarships are not currently available for other projects. Any questions about the research projects should be emailed to: cbdrh@unsw.edu.au
How to apply
Applications close July 20thÌý2025
Once your EOI is received it will be assessed by the supervisory team and you will be contacted to discuss the project and your EOI.
Research projects eligible for a top-up scholarship
- AI in Cardiology
- Fertility, pregnancy, and child health outcomes in women with chronic conditions
- Evaluating health app safety using multi-modal data
- Investigating the mechanism of health apps: how does the user-app relationship impact the clinical outcomes
- Towards Explainable Large Language Models for Clinical Text