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Field
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programme aims to advance fundamental understanding of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key
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plumes from point sources using the MicroHH atmospheric model. Analysis of plume dynamics and NOx chemistry in the high-resolution simulations. Develop and refine data-driven methods for emission
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Your Job: This PhD project focuses on modelling and simulating future gas grids, exploring transformation pathways, and developing cross-sectoral simulation frameworks to support informed decision
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seasonal-to-subseasonal forecasting ensemble in modelling and forecasting these processes. As datasets develop, there may also be opportunities to assess simulation skill of AI forecasts. For further
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, GNSS positioning is highly susceptible to errors from atmospheric distortions, multipath effects, and receiver noise. Recent advances in deep learning have shown that data-driven pseudorange correction
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design recovery and stability strategies using large-scale simulation workflows. Build and expand realistic, continent-scale power system models (e.g., the European transmission grid). Implement and test
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. Multiscale simulations of the downstream expansion behaviour of the engine exhaust plume for different atmospheric layers. Definition of suitable interfaces to process data for the climate models. Expected
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and model inaccuracy. This role is dedicated to solving this problem, building an embedded plume-in-grid contrail model for use in an operational weather model - supporting technological solutions
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carbonated particles; - transformation of monophasic phases to multiphasic; - hydration execution as a cycle of dissolution, diffusion and precipitation. Secondly, the created numerical model will be
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of agricultural innovation, exploring how advanced weather forecasting, crop modelling, and digital decision tools can be harnessed to support smarter, more agile on-farm decisions. It offers a unique opportunity