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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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offers the opportunity to contribute to an emerging area of interdisciplinary research that brings together artificial intelligence and advanced materials modelling to address key challenges in
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AI PhD Scholarship - Opportunity: Foundation Models for Brain Data Job No.: 696618 Location: Clayton campus Employment Type: Full-time Duration: The scholarship may be held for up to 3.5 years
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, candidates could choose to align with, expand upon, or pivot from existing initiatives such as: Example 1: Big Data & Infection Risk Prediction (Stream: Infection Prevention) The Scope: Leverage large, linked
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federated learning and multimodal deep learning models for healthcare. The project will focus on enabling privacy-preserving learning from distributed healthcare data sources, including longitudinal medical
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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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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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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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characterisation, to generate data-rich descriptions of evolving materials and processing pathways. A central aim is to couple these experiments with machine learning, mechanistic modelling, and automated data