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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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early indicators of clinical improvement by identifying patterns of symptom change within the first few sessions, helping to develop heuristics for predicting who is most likely to benefit. The project
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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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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
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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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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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environmental inputs, algae physiological parameters and microbial community eDNA data to develop predictive mechanistic models which can be utilised to develop an optimal cultivation strategy. The project is
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optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics
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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