Machine-learning classification of microbial genovars

The Genome Taxonomy Database (GTDB) has become a leading global framework for genome-based classification of microorganisms.

However, GTDB is based on vertical evolutionary relationships and does not capture traits that occur at the subspecies level or are horizontally transferred between species meaning that it is currently not fit for pathogen classification.

This project will help to develop machine learning classifiers to assign genovar designations to bacterial and fungal pathogens drawing on genome-wide features rather than individual virulence genes alone. This is important because virulence genes may be absent from incomplete genome assemblies commonly recovered from clinical and environmental samples

Completed classifiers will be integrated into the GTDB website as a dedicated genovar metadata field and into the GTDB-Tk standalone software, making them immediately available to the global community of researchers and public health laboratories who already rely on GTDB infrastructure.

The Australian Centre for Ecogenomics (ACE) has access to the National Computational Infrastructure supercomputing and data-storage facilities and the UQ high-performance computing and data environments offering integrated access to numerous bioinformatic databases, commercial software licenses, unique datasets and scientific visualisation tools.

ACE has two PC2-certified laboratories fully equipped for molecular biology, facilitating work from culturing and nucleic acid extraction through to bioinformatics.

This project is supported by the Research project scholarship.

Learn more about the Research project scholarship.

Your application will be assessed on a competitive basis.

We take into account your:

• previous academic record
• publication record
• honours and awards
• employment history.

A working knowledge of machine learning, artificial intelligence, bioinformatics would be of benefit to someone working on this project.

You will demonstrate academic achievement in the field of bioinformatics and the potential for scholastic success.

A background or knowledge of microbiology is highly desirable.

You must submit an expression of interest (EOI) by 5 August, 2026 5 August, 2026.

Before you apply

1. Check your eligibility for the Doctor of Philosophy (PhD).
2. Prepare your documentation.
3. If you have any questions about whether the project is suitable for your research interests, contact Professor Phil Hugenholtz (p.hugenholtz@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the ‘Scholarship/Sponsorship’ section with the following details:

1. Are you applying for an advertised project: 'Yes'
2. Project: 'Research project scholarship'
3. Scholarship Code Listed in the Advertisement: GENOVARS-HUGENHOLTZ
4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/machine-learning-classification-microbial-genovars

Submit an EOI