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PhD Scholarship Develop multimodal machine learning models to predict glioblastoma treatment outcomes using imaging and clinical data. Work with real-world data from John Hunter Hospital in a
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and children's observed birth events and their actual health and well-being outcomes. The latest machine learning and artificial intelligence advancements can mine these datasets to create prediction
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methods, including causal econometric analysis and predictive economic modelling, to assess the economic and distributional impacts of policy interventions across healthcare and disability service systems
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predictive economic modelling, to assess the economic and distributional impacts of policy interventions across healthcare and disability service systems. The project involves close collaboration with
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quantitative methods, including causal econometric analysis and predictive economic modelling, to assess the economic and distributional impacts of policy interventions across healthcare and disability service
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, computational modelling, and data-driven alloy design to: Understand the mechanisms of local austenite-to-ferrite transformation in low-alloy steels; Develop frameworks to predict and control
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the development of predictive models to anticipate potential failures. Additionally, the project will facilitate the transfer of this technology to industry, while also advancing academic knowledge
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will aim to develop novel tools to aid surgical fixation or predict complications and outcomes. Each PhD student will be expected to develop innovative solutions to the problem and publish approx. 3-4
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ability to predict locust band movement. This project will focus on modelling the collective movement of locust hopper bands (thousands to millions of organisms). We will improve on current models through
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" (with Prof Kris Helmerson) "High-bandwidth continuous magnetic sensing of an ensemble of electric spins" (with Prof Kris Helmerson) "Developing a spatially sensitive optical magnetometer catheter probe