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(WEFE) nexus, and model-based learning and communication. The group makes use of a variety of tools and techniques: stakeholder mapping, governance analysis, participatory modeling, non-linear feedback
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language models and continuous wearable data now make genuinely personalised, context-aware coaching feasible for the first time, while raising real questions about safety, equity, engagement, and cultural
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, including situations where some modalities are incomplete or unavailable. Exploring foundation-model and self-supervised learning approaches for extracting transferable representations from large-scale forest
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collaboration with internal and external partners, predictive models including machine learning (ML), materials thermodynamics, etc. for high-throughput identification and screening of advanced materials for gas
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of: electromagnetic sensor design, modelling, prototyping and experimental validation. The best fit is A candidate interested in high-frequency measurement techniques, sensor hardware development and precision
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should be attached to the application describing how your background, skills, and research interests make you a good match for the project on AI foundation models for integrative single-cell and multi
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settings to improve accuracy and confirm the system is fit for purpose. This PhD project will develop, validate and refine a saliva-based hydration assessment system for use in sport and workplace settings
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of coronary artery disease and supporting clinical decision-making during catheterization procedures. The project brings together cardiovascular imaging, generative AI, and computational modeling to develop
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prioritise RD as a mechanism for ecosystem resilience. The project will use Agent-Based Policy Modelling (ABPM) to simulate how actors such as land managers, farmers, local authorities, and conservation
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but is meant to be shaped substantially by the candidate's own interests and expertise. The candidate will cultivate the living organism itself, characterize and model its visible responses to different