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4-year D.Phil. studentship Supervisors: Dr Simone Falco, Prof Daniel Eakins Classic finite elements approach (FEA) approximate the shape of the model using elements with planar faces, therefore
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for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters
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laboratory-scale turbines. Combined with modern measurement methods, these facilities offer ideal conditions for addressing real-world wind energy challenges under controlled laboratory settings. The position
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This project will investigate artificial intelligence (AI) to improve weather forecasts and use crop models for making better farming decisions. The expected outcome is protocols for integrating AI
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of waterlogged conditions, peatlands are projected to be particularly impacted by future climate change, through changes in both temperature and precipitation. Bioclimatic envelope models predict significant loss
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reactivity under conditions relevant to top gas recycling BF with hydrogen gas injection, utilising a range of experimental, analytical, and kinetics modelling techniques. The ideal candidate would have a
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objectives and activities include: • Developing a CFD model to analyse arc physics and establish correlations between transferred energy distribution and deposition conditions (including WA-DED process
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The Institute of Coastal Ocean Dynamics at the Helmholtz-Zentrum Hereon is investigating the observation and quantification of ocean mixing, and its representation in ocean models. We therefore
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Your Job: As part of an interdisciplinary team, you will develop approaches for the automated and large-scale provision and integration of energy systems data and models and apply data science
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. You will employ the trypanosome model established in our group to study its swimming behavior in soft tissue-like surroundings. This project is a part of the DFG-SPP 2332 priority program “Physics