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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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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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these are altered in compromised environments (e.g. due to aging or co-morbidities such as diabetes or chronic kidney disease). This research project aims to develop and use in silico models to simulate the bone
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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 Results: Ability to assess
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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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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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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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models and reinforcement learning models for 3D graphs of materials to explore vast inorganic chemical spaces and design synthesizable energy materials. You will couple such models with physics simulation
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studies, develop sophisticated simulation models, and co-create practical tools with CCs through interactive workshops. Your research will transform theoretical governance principles into actionable
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you excited to use data and simulation models