Bayesian modelling of healthcare data

Are you driven by the challenge of pushing the boundaries in statistical modeling and healthcare research? We invite you to join our innovative PhD project aimed at extending Bayesian spatio-temporal models. This research will integrate individual-level and areal-level data, jointly model multiple outcomes, and expand applications to forecasting health outcomes. You will have the opportunity to apply these advanced models to real-world clinical registries, including lung cancer, prostate cancer, gynecological cancer, the breast device registry, and the bariatric surgery registry, all housed within our School. Additionally, there's potential for collaboration with big-data registries through a fractional appointment. This project not only promises to advance your expertise in biostatistics and Bayesian modeling, but also offers the chance to make significant contributions to improving patient care and health outcomes. If you're ready to embark on a journey that combines rigorous research with real-world impact, apply now and be part of our cutting-edge Biostatistics team. Essential Criteria: 1. Australian Citizen or Australian permanent resident. International students may apply but a competitive scholarship application is required 2. An undergraduate (Honours) or Masters degree in Biostatistics, Statistics, Mathematics. Public Health or related discipline can be considered, especially those with impactful relevant publications 3. High-level analysis skills 4. Familiarity with Stata and/or R 4. Ability to work autonomously as well as collaborate with clinicians 5. Excellent written communication and verbal communication skills with proven ability to produce clear, succinct reports and documents 6. A demonstrated awareness of the principles of confidentiality, privacy and information handling 7. Well-developed planning and organisational skills, with the ability to prioritise multiple tasks and set and meet deadlines Interested candidates who meet the above selection criteria should contact Professor Arul Earnest to discuss their suitability. Minimum entry requirements can be found here: https://www.monash.edu/admissions/entry-requirements/minimum