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MRI, echocardiography, and CT. Another promising approach is the use of cardiac digital twins—mathematical models that simulate a patient’s heart to allow the design and in silico testing of novel
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MRI, echocardiography, and CT. Another promising approach is the use of cardiac digital twins—mathematical models that simulate a patient’s heart to allow the design and in silico testing of novel
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systems (e.g., GC) Experience in synthesis and characterization of heterogeneous catalysts Hands-on experience with (pressurized) chemical reactors Experience with modelling and simulation software (e.g
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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neuroscience and data analysis Proficiency in programming (e.g., Python, MATLAB, and similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural
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response timelines. Building on this foundation, the project will apply scenario modelling and simulation techniques to investigate emergency event propagation, routing strategies, vehicle-task assignment
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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targets the development of advanced coatings to prevent cell-to-cell propagation during runaway events. It combines experimental studies, numerical modelling, and real-world burner rig testing, culminating
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the development of a low fidelity pump model that accounts for unstable and multi-phase flow behaviour through high fidelity simulations. This will be used to develop an integrated fuel system model that will
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this astonishing picometre fabrication precision. Further aims of the project include: Theoretical modelling of nanoscale effects and processes in SNAP Development of experimental methods of picometre-precise