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This PhD project aims to develop and apply innovative photogrammetry-based analytical tools to enhance the spatial and structural characterisation of pharmaceutically relevant materials, with a
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. Project details In this project we aim to develop graph deep learning methods that model spatial-temporal brain dynamics for accurate and interpretable detection of neurodegenerative diseases
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emerging types of national emergencies and evaluate their spatial and operational implications. This will include an analysis of UK population distributions, terrain, infrastructure access, and airspace
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models considering networks of patches and their species and interactions composition to predict spatial and temporal community structure across restoration gradients, aimed at developing a predictive
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framework exploiting the use of physical and geometrical conservation laws in a variety of spatial discretisation schemes (i.e. Finite Element, Finite Volume, Meshless). The resulting conservation-type
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, however, these may not always be able to provide the spatial and/or temporal coverage that is required. In such cases, information from ground measurements can be combined with information from other
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persists, even for the most powerful sensors operating in this way. A drastic departure from this sensing architecture is “multistatic” radar – enacted by a coherent network of spatially distributed sensors
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promise in understanding disease mechanisms and improving clinical decision-making. Recent studies suggest that generative models can uncover latent structures and improve classifier robustness across
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the understanding of offshore turbulence in spatially varying flows. The focus will be on open channel flow dynamics and controlled experimental studies will be designed and conducted to generate and characterise
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. Experimental studies will be performed in wind tunnels with advanced measurement techniques with high spatial and temporal resolutions. Realistic car models (DrivAer models) will be considered in this study and