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Field
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oscillators interacting with body geometry and environment, rather than from centralized digital control. Using a combination of reduced-order models (Hopf/van der Pol/Kuramoto type) and experimental
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-landslides, orographic rainfall effects and extremes), using the volcanic island of Tenerife as a case study. Some work has been done (e.g. on Hawaii), but knickpoint geometry and using state-of-the-art
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successful candidate will join the PhD programme of the Department of Mathematics at the University of Manchester. This 42-month PhD position (funded by the University of Manchester) is a full scholarship
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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representation. Key aims include improving the generalizability, interpretability, reasoning and causal grounding of these models, developing new optimisation algorithms with biologically meaningful regularisation
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-Making and Route Optimisation: Develop adaptive algorithms within a bias-aware ensemble Kalman filter framework to propose alternative flight paths dynamically. The system will aim to maximise safety and
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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence will inform future race strategy and live race tactics. Multiple factors influence the strategy...
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. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis