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modelling capabilities for the prediction of energy extraction efficiency, especially focusing on improving the understanding and prediction of the complex flow phenomena, including buoyancy effects in AGS
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acceptability. D2ET will develop a comprehensive digital platform for planning energy transition scenarios, leveraging a consolidated data model and advanced analytics to facilitate strategic decisions with
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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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PhD Position on Modelling the Evolution of the Larsen C Ice Shelf Faculty: Faculty of Science Department: Department of Physics Hours per week: 36 to 40 Application deadline: 15 May 2025 Apply
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model due to the mathematical challenge of solving the multiple partial differential equations simultaneously. With the support of the combined sponsorship from the university and industrial partner
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desorption mechanisms of various FFA on different type of metallic surfaces as a function of temperature and concentration. The modelling data and principal component analysis will be used to build property
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promise in understanding disease mechanisms and improving clinical decision-making. Recent studies suggest that generative models can uncover latent structures and improve classifier robustness across
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26th May 2025 Languages English English English The Department of Structural Engineering has a vacancy for a PhD Candidate in Multiscale modelling of grain boundary failure in high-strength
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preferably has strong programming skills and experience with the modeling and simulations of fluid or solid mechanics or ice sheet flow and deformation (for example by use of finite element/volume methods
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This project provides an excellent opportunity to develop state-of-the-art mathematical models and computational tools of emerging respiratory pathogens, within a world-renowned research group