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Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods
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and Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods
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implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation
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information, for example data derived from remote sensing, use point process models from the field of spatial statistics to model clustered patterns across the landscape, and develop methods for estimating
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this, the postdoc will use innovative modeling based on the coupling of (1) a meteorological model adapted to the fine spatial scales of these systems and explicitly simulating turbulent structures, and (2) a
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A crystal in space represents a distinct state of matter, with spatial periodicity in its lattice structure underpinning its band structure and optical properties. This project concerns time
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development. It will involve a paradigm shift which combines geo-spatial-temporal modelling, prospective life cycle analysis, techno-economic assessment and AI methodologies. It will map and analyse
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and spatial conditions influence energy performance and thermal comfort. By combining spatial mapping, data modelling, and fuel poverty metrics, the project will identify where health risks are most
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leverage large-scale AI models to: integrate heterogeneous EO data sources, such as satellite, aerial and in-situ data, across spatial and temporal scales; enable zero-shot or few-shot learning for rapid
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spatial transcriptomics, with the ultimate goal to understand and harness immune responses in human diseases. The Engblom and Villablanca labs form a tight-knit collaborative team of scientists with diverse