Machine learning models for clinical registries

Are you passionate about leveraging machine learning to revolutionize healthcare? Join our cutting-edge PhD project focused on applying advanced machine learning techniques to clinical registries. This PhD project will involve developing and refining machine learning models to analyze data from registries such as the Australian Diabetes Clinical Quality Registry, Australian Cystic Fibrosis Data Registry, and Australian Breast Device Registry. You'll get to choose to focus on a few critical issues like hyper-parameter tuning, feature selection, handling missing data, sampling and target balancing, with an aim to create robust models that can predict health outcomes and identify early warning signals for systematic shifts in patient care. If you're eager to work at the intersection of data science and healthcare, and contribute to meaningful advancements in medical research, apply now to join our innovative team. Make a difference in patient care through the power of machine learning. 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