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PhD Scholarship – Modelling the social and political drivers of net zero transitions Job No.: 670767 Location: Clayton campus Employment Type: Full-time Duration: 3.5-year fixed-term appointment
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deep learning theory, Bayesian statistics, and generative modelling, this work will advance our understanding of both the capabilities and vulnerabilities of modern AI systems. This will have potential
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Current reseach is in the areas of: Development of biomimetic structures as ultrasound contrast agents Deep tissue imaging using photoacoustic contrast agents All optical photoacoustic sensors
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interaction and human motion analysis Prior knowledge of machine learning/deep learning applied to motion analysis (e.g., relevant courses and research experience) would be an advantage IELTS score of 6.5
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of cities. Develop the first national land use–transport interaction (LUTI) model for Australia. Evaluate policy scenarios involving HSR to realign population growth with sustainability goals. The selected
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to the advancement of healthcare technologies, systems, and services through applied design practice. With a portfolio spanning mobile imaging, wearable technologies, and distributed models of care, DHC leads
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-reality application development; modern AI techniques (such as computer vision or large multimodal language models); and/or human-computer interaction. Our industry partners are developing software
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-tracking, pupillometry), cognitive modelling, and regulatory analysis to assess how algorithmic explanations shape human judgement and how existing legal and ethical frameworks align with the evolution
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-style interventions, and preventative medication. Analysis will utilise best practice in health inequalities measurement, modern econometric techniques, behavioural experiments, and modelling
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some modelling experience will be a distinct advantage. Experience with other interfacial characterization techniques would also be beneficial especially in the absence of scattering experience