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Primary supervisor Zhaolin Chen Research area Data Science and Artificial Intelligence Portable point of care medical devices have revolutionised the way in which people receive medical treatment. It can bring timely and adequate care to people in need but also opens up the opportunity to...
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
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
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scholarships funded by a national elite HDR training program: Data61 Next Generation Graduate (https://www.csiro.au/en/work-with-us/funding-programs/programs/next-generation-graduates-programs), and
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inference. Problems : In order to enable on-device ML, models must be deployed at the local mobile devices, thereby creating a new attack surface inevitably. Commercial ML models are now stored on mobile
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inference. Problems : In order to enable on-device ML, models must be deployed at the local mobile devices, thereby creating a new attack surface inevitably. Commercial ML models are now stored on mobile
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and understanding protein structures, but expert supervision is required in the development, training and deployment of these models into automation scenarios. We seek to develop a human-centred AI
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pursue a deeper understanding of humans and machines and the meaning this has for our future and the development of safe AI. To do this project you would need to apply for a Monash Scholarship. Required
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pursue a deeper understanding of humans and machines and the meaning this has for our future and the development of safe AI. To do this project you would need to apply for a Monash Scholarship. Required
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known oncological pathophysiology. LIME (Local Interpretable Model-agnostic Explanations): Generates patient-level attributions, showing clinicians the exact weight and direction of individual lab
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known oncological pathophysiology. LIME (Local Interpretable Model-agnostic Explanations): Generates patient-level attributions, showing clinicians the exact weight and direction of individual lab