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Field
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on the performance of the CMF; Using machine-learning (deep learning) methods to develop a predictive model and conduct the sensitivity study to investigate the multiple factors on the performance of flow meter
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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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the intersection of ecology, machine learning, and sustainable land management, the research will combine field data collection, deep learning model development, and stakeholder co-design to support biodiversity
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
will train machine learning models to identify and assess internal defects with greater accuracy and speed than traditional methods. The results will support predictive maintenance, reduce inspection
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under multiple environmental and socio-economic scenarios. You’ll develop sought-after skills in geospatial analysis, hydrodynamics, sediment transport, machine learning-assisted detection, and hydro
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, fairness). Provenance and integrity of machine learning pipelines. Generative content authenticity. Cyber-physical machine learning systems. Scalability of properties from small to large models. In
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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accelerators originally designed for artificial intelligence. These accelerators achieve exceptional performance by using low precision arithmetic, which is sufficient for machine learning tasks but much too
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built to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine
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configurations and illumination conditions. Implement and validate device-independent representations. Investigate and apply domain adaptation and transfer learning techniques to develop models that generalize