PhD Scholarship available

The University has awarded a special round of Signature Research Theme (SRT) scholarships. My project was successful, so I am looking to recruit a student. I have pasted the title and description below. This is rather heavy on buzzwords, which seemed to be required by the Uni, but don’t let that put you off. This is a project at the intersection of applied mathematics, machine learning and Bayesian statistics.

The main selling point is that this scholarship is open to both domestic and international students. There will not be any other international scholarships offered this year, so this is quite a rare opportunity.

To apply, you need to complete an EOI form here. The project ID is: SRTSR0293, and the deadline is the 30th of September.

If you have any questions about the details of the project please email me.

Real-time forecasting of respiratory disease burden: scalable machine-learning methods for health-system planning

Each winter, Australia faces overlapping epidemics of influenza, COVID-19 and RSV that strain hospitals, especially when their peaks coincide. Knowing when and how high these epidemics will peak would let health authorities plan ahead and move resources before surges hit, but current methods forecast poorly beyond a certain time frame.

This project will develop machine-learning methods that make richer, more realistic epidemic models fast enough to use in real time. The student will train neural networks that stand in for the most demanding computations these models require, then use them to forecast respiratory disease across Australian states. They will join the Australia-Aotearoa Consortium for Epidemic Forecasting & Analytics and the forecasts will feed into a national program reporting weekly to every state and territory health authority. The project builds transferable skills in machine learning, statistics and epidemic modelling, at the interface of mathematics and public health with direct real-world impact.

This project would suit applicants in Mathematics, Statistics or Physics. Knowledge of machine learning would be useful, but is not essential.