An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation

Full Description

This project develops an AI-informed, community-grounded framework to understand and mitigate antimicrobial resistance (AMR) risks within a planetary health context.

Building on the risk modelling from Project 1, we will conduct focus groups and interviews with communities in high- and low-risk zones to capture local knowledge, behaviours, and risk perceptions related to AMR.

In parallel, key informant interviews with policymakers from the Ministry of Health, Department of Environment, and local councils will map current policies, surveillance gaps, and opportunities for intervention.Insights from these engagements will be integrated with outputs from advanced AI models (e.g.,geospatial deep learning, graph neural networks, and explainable AI ) to refine the AMR risk maps and ensure local relevance.

Supervisors:

Main Supervisor Professor Wong Kok Sheik, Monash University

Co-supervisors:

Dr Sicily Ting Fung Fung, Dr Patrick Tan Hock Siew, Dr Sara Subhan, Dr Qasim Ayub, Dr Aswini L.

Loganathan

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How to Apply

It is suggested that you first contact the main supervisor and provide them with your academic background and achievements to determine whether you are a 'fit' for this research topic.

If you feel you are a 'fit', please click here to complete an Expression of Interest, including your research proposal relevant to this project.

Your EoI will be assessed and if you are eligible, you will be invited to apply for PhD candidature and may be selected to interview for the scholarship.

Interviews are likely to take place in February 2026 with successful applicants notified shortly afterwards.

Scholarship open until filled.