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Field
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afraid of combining neurobiology and chemistry. You have good statistical skills and experience with analyzing big data (e.g. RNA-seq, spatial transcriptomics). You like to work in a diverse setting and
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architectures [5] and Neural ODEs [2], creating a cohesive end-to-end hydrological model. Transformers will be utilized to pre-process and analyse the raw input data, capturing essential spatial and temporal
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reputation for carefully designed and precisely controlled experiments, high quality temporally and spatially resolved field experiments using particle image velocimetry combined with synchronised measurements
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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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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
-microscopy, proteomics and metabolomics. This project will study large protein secretion systems, drug-efflux pumps and lipid transport towers to provide means to integrate spatial information and metabolic
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for estimating sustainable city size by identifying key determinants (e.g., accessibility, agglomeration, emissions, equity), testing spatial boundary sensitivity, and broadening sustainability
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, spatial transcriptomics). You like to work in a diverse setting and communicate closely with a diverse and multidisciplinary team (i.e. neuroscientists, chemistry). You are a team player and a pleasant
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other leading corporates. Transportation data are inherently spatial, temporal, multi-modal, and high-dimensional. Our work addresses the challenges of Perception, Decision, and Explanation (PDE) in
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support to competent authorities on how to include shipping pressures and impacts in marine environmental management and spatial planning. Research environment Our research aims at supporting sustainable
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exposome and dynamic exposome modeling, learning in timeseries and spatial data, and hybrid deep learning-causal modeling. The successful applicant should have significant research experience in at least two