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. The Opportunity This interdisciplinary PhD project between IT and the social sciences will explore the development of computational approaches for detecting and analysing misogynistic backlash ecosystems across
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for infrastructure platforms in minerals-to-green metals transformation. It will suit a candidate interested in combining experimental materials science, advanced characterisation, and data-driven modelling to develop
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resources to avoid downtime, adjusting dynamically as traffic fluctuates. For researchers and students, this component focuses on developing ML models to predict resource needs, improving load distribution
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traditional and advanced optimization techniques, including analytical models, simulation-based approaches, and data-driven algorithms. The research also considers practical constraints such as cost, process
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often provide dietary advice based on the average responses of groups to specific foods, rather than considering individual glycemic responses. This project aims to develop an AI-driven, personalised
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, spectroscopy, astrometry) using massive optical telescopes on Earth and in space (e.g., Hubble, Gaia, JWST, Kepler, TESS). My group develops cutting-edge models to extract the most from noisy data and to better
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cutting-edge AI methodologies, focusing on combining data-driven approaches with physics-informed models to tackle challenges in MRI reconstruction. By integrating MRI acquisition physics directly into
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visuals, performing spatial and temporal reasoning, and managing multi-step decision-making tasks. By developing specialised models for spatial reasoning and enhancing the integration of map-based tools