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Field
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Modern numerical simulation of spray break-up for gas turbine atomisation applications relies heavily upon the use of primary atomisation models, which predict drop size and position based upon
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overnight stay for post anaesthesia recovery. The needed post-operative care is predicted manually based on a pre-operative assessment for surgeries case selection to be scheduled for each day. This decision
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics and PhD students, and communicate your research at national
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neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and
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continuously since we have been producing it. Our brains work by generating and testing predictions – but younger brains, which are messy and inefficient, are presumably less good at ‘keeping up’ with fast-paced
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– i.e., the light, volume and pitch changes from which we extract meaning – has increased continuously since we have been producing it. Our brains work by generating and testing predictions – but younger
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which we extract meaning – has increased continuously since we have been producing it. Our brains work by generating and testing predictions – but younger brains, which are messy and inefficient
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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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are often issued one month in advance, but there is limited research on predictability of cyclones on lead times longer than this. This research will use the latest generation of seasonal and decadal