The human microbiome consists of pathogens and beneficial microorganisms that mutate within a person billions of times each day. Host-microbial interactions play a critical role in our health and can have profound impacts on the treatment of both infectious and non-infectious diseases, including cancer. However, microbial genomics studies generate enormous datasets, and the evolutionary dynamics of the human microbiota present major challenges to unravelling the interactions between microorganisms, host cells, and the environment. This project will develop cutting-edge statistical and machine learning models to identify fine-scale associations between genomic features of bacteria and human disease. The research scope can be tailored to the candidate’s interests, with potential focus areas including:

• Prokaryotic protein annotation and clustering
• Phylodynamics and transmission inference
• Within-host evolution
• Microbial genotype-to-phenotype association studies

Analyses will focus on unique longitudinal metagenomic and pathogen sequencing datasets to examine the effects of drug administration, including antibiotics, supplements, and cancer treatments, on bacterial evolution and their interactions with the host.

Candidates with a strong background in statistics, computer science, bioinformatics, or a related discipline are encouraged to apply. This is an excellent opportunity for students interested in contributing to cutting-edge research at the intersection of microbiology, genomics, statistics and machine learning.

The project will be supervised by Dr. Gerry Tonkin-Hill at the Peter MacCallum Cancer Centre and the Peter Doherty Institute, University of Melbourne. This fully funded position is open to both local and international students. Prospective candidates are encouraged to contact Gerry (gtonkin [at] unimelb.edu.au) before submitting their application. Final applications must be submitted to the University of Melbourne by October 31st 2024.