Helena Teede

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.

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.