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Bayesian modelling of healthcare data

This PhD will extend the work that is already done in terms of developing Bayesian spatio-temporal models which incorporate individual level data and extend the application to forecasting health outcomes using simulation studies. The models will be applied to real-life clinical registries such as lung cancer, prostate cancer, etc housed within the School. There is also the potential to work with a fractional appointment with big-data registries. The PhD candidate should ideally be someone who has a background in a quantitative field such as Statistics, Biostatistics, Mathematics or MPH. Essential Criteria: 1. Australian Citizen or Australian permanent resident. International students may apply but they would need to apply for a scholarship 2. An undergraduate (Honours) or Masters degree in Biostatistics, Statistics, Mathematics, Public Health or related discipline 3. High-level analysis skills; familiarity with Stata and/or WinBUGs program 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 Associate Professor Arul Earnest to discuss their suitability. Minimum entry requirements can be found here:
Essential criteria: 
Minimum entry requirements can be found here:
Bayesian, spatio-temporal, forecasting
School of Public Health and Preventive Medicine » Epidemiology and Preventive Medicine
Available options 
Time commitment 
Top-up scholarship funding available 
Physical location 
553 St Kilda Rd, Melbourne (adjacent to The Alfred)

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