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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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interventions. Potential research activities include: Developing prediction models for receptivity and lapse Conducting micro-randomised or n-of-1 trials Evaluating psychological mechanisms of engagement and
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approaches to explore and predict new semiconductor materials and their properties. What makes this opportunity special? Earn a Doctor of Philosophy from Monash University and the University of Bayreuth
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Webb, developing AI systems that can understand a continuously changing world. The Temporal Analytics Lab focuses on understanding patterns, making predictions and detecting unusual behaviour in data
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I supervise projects in particle physics. My main emphasis is on phenomenology, comparison of predictions with experimental measurements. I follow developments in flavour physics: weak decays
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comparing our experimental observations to predictions made using the Standard Model of Particle Physics. I am a member of the LHCb collaboration, one of the four large experiments at the Large Hadron
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learning to improve disease diagnosis, risk prediction, and clinical decision support. Applications will include chronic diseases such as cancer, diabetes, and rheumatoid arthritis. This research forms part
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recognition Individual and group behaviour understanding Long-term behaviour prediction Multi-task learning and foundation models Zero-shot and open-set recognition Privacy-preserving AI using anonymised human
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of their remnants (including predictions for GW sources); mixing and transport processes in the stellar interior; nucleosynthesis and the origin of elements, including galacto-chemical evolution - which elements
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engineering. Experience with experimental measurements, image-based diagnostics, laboratory testing or quantitative data analysis. Coding and analysis capability in Python and/or Julia, preferably with