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in reviewing, testing, refining, and providing feedback on historical records that are automatically transcribed, coded, and linked using computer algorithms. Support the project and technical team in
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deep learning algorithms in ESRI ArcGIS or similar software. Desirable Application Proficiency with relevant specialised software and approaches (e.g., geographic information systems, high-performance
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processes that could be realised in neuromorphic hardware. The research will combine theoretical derivation and simulation-based validation, using mathematical modelling, algorithmic experimentation, and
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. This PhD project addresses that challenge by designing and optimising a dual-purpose battery energy storage system (BESS) with two complementary functions: (1) storing and distributing energy for stationary
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-to-failure dataset is then fed into powerful Artificial Intelligence algorithms, particularly time-series Neural Networks. These models learn the complex sequence of events that reliably precedes performance
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. Successful re-development for end-of-life composites could enable reuse in other structural applications. This PhD will investigate the development of hierarchical Bayesian algorithms to capture
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models of the additive manufacturing process. Surrogate models to accelerate virtual material testing and verification will be developed to allow the generation of property distributions from process
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distributed tasks such as inspection of a big geographic area for fires is an important objective of this project. The project aims are to: 1) provide methodological contributions towards intelligent sensing
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(KBAs). However, these networks are largely based on current species distributions. As climate and habitats change, species ranges are shifting—posing a fundamental question: will today’s conservation
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uncertainty about the effectiveness of a drug using a “prior” probability distribution, before the trial is conducted. This distribution is used to assess the likelihood that a trial will produce a successful