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in radiation–matter interactions, computational modelling, and materials science, with a strong publication record (h-index 36, i10-index 69). Dr Francesco Fanicchia, Research Area Lead: Material
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not fully capture the high-temperature, complex thermal-fluid interactions within the pebble-bed. This PhD project will focus on advancing porous media models for pebble-bed HTGRs by leveraging newly
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Proteins are key biomarkers, indicative of normal biological or pathogenic processes and responses to intervention. Identification and quantification of such molecules in biological fluids is
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Establishment). Recent work by the group (leading to REF 4* rated outputs and several Keynotes) has contributed to bridging the gap between Computational Solid and Fluid Dynamics, with a unified computational
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
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nanosheets, nanotubes, etc) or hybrid (e.g. boron carbon nitride). Similarly, while water is the most studied coolant liquid, realistic applications involve dielectric fluids (e.g. benzene, pentane). Molecular
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Location: South Kensington About the role: The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple
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hydrogen pumping system. Regenerative pumps are not currently used for pumping volatile cryogenic fluids such as liquid hydrogen. The objective of this project would be to develop a structured design
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Project Background: Why some people with multiple sclerosis (MS) experience faster changes in brain structures (neurodegeneration) than others? What genetic associations with brain regional