This PhD opportunity at Monash University Malaysia focuses on developing an AI-informed, community-grounded framework to understand and mitigate antimicrobial resistance (AMR) risks within a planetary health context. The project builds on previous risk modelling and incorporates qualitative research through focus groups and interviews with communities in both high- and low-risk zones. These engagements aim to capture local knowledge, behaviours, and risk perceptions related to AMR, providing a nuanced understanding of the factors influencing resistance.

In addition to community input, the project involves key informant interviews with policymakers from the Ministry of Health, Department of Environment, and local councils. This policy mapping will identify current strategies, surveillance gaps, and opportunities for intervention, ensuring that the research is both locally relevant and actionable.

Advanced AI techniques, including geospatial deep learning, graph neural networks, and explainable AI, will be used to integrate insights from community and policy engagements. The goal is to refine AMR risk maps and develop targeted mitigation strategies that are informed by both cutting-edge technology and local realities.

The supervisory team includes Professor Wong Kok Sheik (main supervisor), Dr Sicily Ting Fung Fung, Dr Patrick Tan Hock Siew, Dr Sara Subhan, Dr Qasim Ayub, and Dr Aswini L. Loganathan, offering expertise across biomedical engineering, AI, genomics, and public health. The project is housed within the Department of Engineering and Information Technology, providing access to interdisciplinary resources and support.

Successful applicants will be awarded a Graduate Research Excellence Scholarship (GRES), which includes a full tuition fee waiver and a monthly stipend for up to 3 years 6 months. To be eligible, candidates must hold a First Class Honours (H1) degree or its equivalent, as recognised by Monash University Malaysia, and meet the English language requirements. A strong background in bioinformatics, biotechnology, genomics, artificial intelligence, computer science, or biomedical engineering is preferred.

The application process involves contacting the main supervisor with your academic background and achievements to assess your fit for the project. If suitable, you will be invited to complete an Expression of Interest and submit a research proposal. Interviews for shortlisted candidates are expected to take place in February 2026, with the scholarship open until filled. For more details and to apply, visit the project page.