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About us The School of Neuroscience is UK’s 2nd largest Neuroscience school with over 500 researchers and 200 PhD students. It is one of three schools at the Institute of Psychiatry, Psychology & Neuroscience. CDN is one of four departments in the School of Neuroscience at the Institute of...
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King's College London Department of Engineering | London, England | United Kingdom | about 1 month ago
science interlink prevention and prediction of wildfire risk, by contributing to the development of a fundamental physical model to understand the process of fire spread for wildfires, as part of a European
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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-treatment facilities, and biorefineries. Feedstock choice, regional dynamics, and process side-streams all affect costs, energy use, and emissions. This PhD project will develop advanced computational models
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scattering with computer modelling such as molecular dynamics simulations and AI-assisted data mining. The new technical capabilities will help bridge the current gap in biocide development, i.e., to link
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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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manufacturing (3D printing) techniques. The purpose of the studentship is to develop a next-generation in vitro model of aged human skin to evaluate the cytocompatibility of materials used in maxillofacial
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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a comprehensive, multi-fidelity suite of liquid hydrogen (LH2) pump models to predict and analyze pump performance, stability, and its interaction with the broader fuel system architecture for a