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a comprehensive, multi-fidelity suite of liquid hydrogen (LH2) pump models to predict and analyze pump performance, stability, and its interaction with the broader fuel system architecture for a
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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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/Associate will be part of Newcastle University’s Electrical Power Group working with the Marine Resources and Renewable Energy Group. The aim of the role is to assist in the design modelling and build of a
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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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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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, a state-of-the-art process-based model for groundwater risk assessment and contaminant transport modeling. By improving predictive modeling of transient contaminant source terms, this research will
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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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prediction model for stage racing that can be used to inform live race decisions based on emerging events. The successful candidate will also spend time embedded in a professional cycling environment and gain
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resulting properties. However, two significant challenges persist in this domain. First, the extrapolation of ML predictions beyond the range of existing data remains problematic, as models often struggle
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, current models only predict the potential for events rather than actual specific landslide occurrence. These models also struggle to directly quantify landslide hazards and to address key characteristics