Research Projects

Recent advances in sequencing technologies have dramatically increased the amount of genome data available for many organisms. Because many microbial pathogens, such as viruses and bacteria, such genome data are informative about their transmission dynamics, a field known as infectious disease phylodynamics. A key challenge in phylodynamics is coupling genome sequence data with other sources of information, such as epidemiology, drug resistance profiles, and geographic location. Ideally, these different sources of information will improve the range of inferences in phylodynamics. These inferences are particularly important for genome surveillance initiatives, where genome sequences and associated metadata are routinely generated to understand the evolution of infectious pathogens in near-real time.

This project has two broad aims; (i) to develop Bayesian hierarchical models to integrate different types of data in phylodynamic analyses, and (ii) to leverage these frameworks for genome surveillance where computational speed and assessing statistical power are essential. This project has a strong methodological component, but it will capitalise on existing data of influenza viruses, HIV, and outbreak-causing bacteria generated at the Doherty Institute and through a network of external collaborators (University of Sydney and University of New South Wales). There are ample opportunities with international collaborators (ETH Zurich, Institut Pasteur and University of Auckland), including leading developers of Bayesian phylogenetic methods, such as BEAST. A good understanding of phylogenetics and Bayesian statistics is essential, and programming skills in at least one scripting language (e.g. R or Python) are highly desirable. There are opportunities for a PhD stipend or top-up for highly motivated candidates.

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