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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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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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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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sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data
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search strategies 3) How to leverage the spatio-temporal diversity of multistatic radar observations At the end of the PhD an over-arching modelling environment will be built, where the parameters above
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Application deadline: 01/08/2025 Research theme: Turbulence, Fluid Mechanics, Offshore Conditions, Renewable Energy, Hydrodynamics, Experiments This 3.5 year PhD is fully funded for applicants from
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We are inviting applications for a fully funded 3.5-year PhD Computer Science studentship at the University of Warwick, jointly supported by GlaxoSmithKline (GSK), to work on an ambitious project
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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
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enrichment strategies can be tailored for best conservation success. This PhD project will combine empirical fieldwork with cutting-edge spatio-temporal ecological modelling of systems dynamics to investigate