PhD Scholarships in Clinical Risk Prediction, AI/ML and Big Data for Women’s Health at Monash University Monash University in Canada

PhD scholarships in clinical risk prediction, AI/ML and big data for women’s health at Monash University

The Monash Centre for Health Research and Implementation (MCHRI) is advertising two PhD scholarship opportunities in a multidisciplinary project focused on clinical risk prediction, artificial intelligence/machine learning, big data, and women’s health, with a specific application to diabetes in pregnancy and related maternal health outcomes.

The project aims to update and evaluate clinical prediction models to improve personalised, timely, and equitable care. Students will work with large linked health datasets, biomarkers, screening approaches, and advanced quantitative methods. The research environment is strongly methodological and translational, with training in clinical prediction modelling, biostatistics, machine learning, data and biomarker analysis, health economics, implementation science, and translation into practice.

Two project streams are available: one focused on data and biomarker analysis for risk prediction, and another on risk prediction model updating, evaluation, and translation. Outputs are expected to include high-impact publications, validated risk tools, and contributions to improved care pathways in Australia.

Eligibility: Applicants with backgrounds in biostatistics, epidemiology, machine learning, public health, medicine, nutrition/dietetics, or related quantitative fields are encouraged to apply. Strong analytical skills and interest in applying advanced quantitative methods to clinical and public health problems are important. Australian citizens or permanent residents are especially encouraged.

Funding: The scholarship includes an Australian Government RTP stipend, a domestic top-up of $3,000 per year for three years, possible paid research assistant hours, and conference funding up to $2,300.

Location: Clayton, Melbourne, Australia.

How to apply: Review the project page and minimum entry requirements, then submit an expression of interest using the linked application page. The post directs applicants to the Supervisor Connect project page for more details.