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datasets with phylogenies and environmental variables, the project aims to rapidly explore trait evolution, predict dispersal potential, and assess climate-related risks. This work bridges biodiversity
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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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United Nations Sustainable Development Goal 15 aims to protect terrestrial ecosystems, manage forests sustainably, combat desertification, and halt biodiversity loss. Achieving this requires a
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, there is a critical need to develop and implement life cycle carbon accounting tools and optimization techniques tailored to the specific operations and materials used in the pipe industry. This PhD is co
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. Development of novel processing techniques Modelling techniques that can inform the direction of experimental activity Physical, mechanical and materials characterisation techniques Data-driven approaches
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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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heat flux. Design, modify, and test novel heating surfaces that more accurately replicate industrial conditions. Develop advanced post-processing methods to extract key local quantities associated with