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Field
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and focuses on cellular crosstalk in the regulation of liver function in health and disease. For this we apply single cell- and spatially resolved transcriptomics and bioimaging to study human liver
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actors involved in managing public space? How can they adapt to dynamic societal needs and increasing spatial interdependencies? What institutional models promote more efficient, equitable and resilient
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. This requires an understanding of material flows and qualities. This project will develop the tools to model in sufficient detail the steel flows and evaluate the role and contribution of changes in (production
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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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. Your job In this PhD position, you will conduct idealised experiments with an atmospheric model (OpenIFS), using concepts from the mathematical field of periodically forced dynamical systems. You will
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ideal candidate will have an interest in ecological modelling, spatial analysis, and conservation planning, with experience in ecological fieldwork or quantitative data analysis being advantageous. Full
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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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to include shipping pressures and impacts in marine environmental management and spatial planning. Research environment Our research aims at supporting sustainable development of the maritime shipping sector
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. However, current estimates of methane emissions from inland waters to the atmosphere are highly uncertain because of limitations in long-term observational data and modelling methodology. In this four-year
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the physical and biological pumps during rapid climate transitions (e.g., the last glacial period and Holocene) using sediment records. Our data will be used in marine carbon cycle models to predict