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Combating antimicrobial resistance through use of artificial intelligence and genomics

Primary supervisor

David Dowe


  • Nenad Macesic

Antimicrobial resistance (AMR) is one of the most significant and immediate threats to health in Australia and globally. We are working on harnessing new technologies such as artificial intelligence and next-generation sequencing and to improve the diagnosis, treatment and prevention of AMR infections.


The specific aims of this project are:

1. Rapidly identify AMR and predict treatment responses through use of genomics and machine learning in a clinical context.

2. Detect healthcare-associated transmission of AMR in real-time and transform outbreak response through use of novel long-read sequencing.

3. Use predictive approaches to personalise therapy of patients at-risk or affected by AMR pathogens. This will be achieved by integrating cutting-edge techniques in bacterial genomics, including both short-(Illumina) and long-read sequencing (Oxford Nanopore), data mining of electronic medical records and use of machine learning to predict several outcomes.


Assoc. Prof. David Dowe will be the primary supervisor for this project.



Dr Nenad Macesic

Prof Anton Peleg

Required knowledge

Minimum entry requirements can be found here:

A knowledge of mathematics (e.g., calculus, derivatives, partial derivatives - and matrices and determinants) and statistics (e.g., likelihood and log-likelihood) will be important.  A knowledge of any of machine learning, data science and information theory will be useful.

Learn more about minimum entry requirements.