PhD in Biostatistics and Women's Health: Personalised Prediction of Premature Ovarian Insufficiency

Monash University’s Centre for Health Research and Implementation is offering a fully funded PhD opportunity focused on the personalised prediction of premature ovarian insufficiency (POI) using clinical and biomarker data. This MRFF-funded project addresses a significant gap in women’s health: the lack of a validated clinical tool to predict individual risk of POI or early menopause, which affects approximately 16% of Australian women before age 45. The successful candidate will contribute to developing Australia’s first personalised POI risk prediction model, integrating clinical, demographic, and biomarker data to create a clinically implementable decision-support tool. The project involves applying advanced biostatistical and risk modelling methods to derive, internally validate, and prepare for external validation of the model. The research will directly contribute to equitable, preventive women’s healthcare and produce high-impact methodological and applied publications. The candidate will join a high-performing supervisory team with expertise in biostatistics, reproductive epidemiology, and women’s health translation, including Dr Yitayeh Belsti Mengistu, Prof Helena Teede, Dr Alicia Jones, and Associate Professor Joanne Enticott. The position is based full-time at the Clayton campus and is supported by an Australian Government Research Training Program (RTP) stipend. Domestic candidates receive a $3,000 per annum top-up for three years, with opportunities for additional paid research assistant hours and up to $2,300 in conference funding. This package provides strong financial and professional development support within a nationally funded research program. Applicants should have a strong Honours or Masters degree in biostatistics, epidemiology, data science, or women’s health, and meet Monash University’s minimum PhD entry requirements. The project is ideal for candidates with strong quantitative skills and a passion for women’s health research. For more information and application instructions, visit the Supervisor Connect project page or contact the supervisory team.