PhD in Clinical Risk Prediction, AI/ML and Big Data for Diabetes in Pregnancy Monash University in Canada

PhD opportunity at Monash University in clinical risk prediction, artificial intelligence/machine learning, big data, biostatistics, and maternal health, focused on diabetes in pregnancy.

This interdisciplinary project develops and evaluates personalised prediction tools to improve risk stratification, timely care, and equitable outcomes for women with gestational diabetes mellitus and related pregnancy/postpartum risks. The work sits at the intersection of clinical medicine, epidemiology, biostatistics, biomarker science, health economics, implementation science, and data science.

Successful candidates will join the Monash Centre for Health Research and Implementation and work with a multidisciplinary supervisory team including Assoc Prof Joanne Enticott, Dr Yitayeh Belsti Mengistu, and Prof Helena Teede. The project involves analysing large linked healthcare datasets, developing and validating prediction models, and translating findings into practice.

Funding includes an Australian Government Research Training Program stipend, with a top-up scholarship for domestic candidates for 3 years, possible paid research assistant hours, and conference funding up to $2300.

Applicants should have a quantitative background such as biostatistics, statistics, data science, computer science, artificial intelligence, epidemiology, bioinformatics, clinical epidemiology, public health, medicine, or nutrition/dietetics. Prior experience in predictive modelling, statistical programming, or machine learning is advantageous. Minimum entry requirements apply.

Apply via the project link and review Monash University entry requirements before submitting an application.