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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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structural alloys. The project will combine advanced phase-field fracture mechanics, continuum-scale chemo-thermo-mechanical modeling, and advanced machine learning techniques for enhanced prediction accuracy
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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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resource-constrained environments, and it is important to investigate whether features derived from different network layers can be effectively combined. Machine Learning Model Development & Optimization
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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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brings together expertise in health data science, microbial genomics, and cancer bioinformatics. Th selected student will work under the supervision of Dr Arron Lacey, a specialist in machine learning and
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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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. 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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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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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