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Field
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. Applying machine learning to New Zealand’s landslide inventories to model landslide location, character and dynamics. Integrating time-series and inventory data to develop new models to predict location
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. The system will leverage cutting-edge techniques in Natural Language Processing (NLP), Machine Learning (ML), and Multimodal Analysis to conduct adaptive interviews, assess candidate responses, and generate
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. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large
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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites
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machine-learning surrogate models capable of delivering near-DFT (density functional theory) accuracy in just a few CPU seconds per structure. This approach will enable the high-throughput screening of tens
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learning algorithm to develop an ability to choose what main data pattern/structure to preserve? This PhD project will approach this question by developing modelling strategies and pipelines to enable human
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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to begin focused research early on. To complement their background, optional training in natural language processing, machine learning, and linguistics will be available. During the first year, the student
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/ . The post offers an exciting opportunity for conducting internationally leading research on the whole spectrum of novel machine learning algorithms and practical medical imaging applications, aiming
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untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis