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in size, composition, or ligand shell to drive the formation of novel phases. This will enable the discovery of new lattice types, defect-tolerant arrangements, and collective behaviours not accessible
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for the new green steels compositions, including impurities and tramp elements. These models should enable density-functional-theory (DFT) accurate large scale atomistic simulations of defects including
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described in the project overview. Owing to the current composition of the project team, there will be a mild preference for candidates opting for project 2 on “Models and machine learning”. An explanation
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to be cost competitive with other technologies, long lifetime of >5 years operation under high current density is desired. Operation conditions such as temperature, gas composition, current density and
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corrosion-fatigue conditions by integrating multiscale physics-based models combined with mesoscale experimental tests. This research will study the effects of corrosion-induced changes in composition
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to scrap-based EAF steelmaking, by using a high percentage of scrap supplemented with ore based metallics (OBMs), is an attractive route to decarbonise the steelmaking process. However, residual elements
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responses and recovery in humans. To this end we combine data from psychopharmacological experiments in healthy participants and real-world clinical data from surgery patients for a more in-depth
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the formation rates or composition of a biominerals from known environmental conditions. This project aims to construct such a model. The ultimate goal is to create a general framework for predicting
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-metallic clusters that combine plasmonic and catalytic metals These clusters will be deposited with high control over size and composition using cluster beam deposition on morphologically engineered TiO2
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healthy participants and real-world clinical data from surgery patients for a more in-depth understanding of stress and resilience mechanisms. The project is an interdisciplinary effort bridging psychology