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properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability
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properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability
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brain decoding methods and test the extent to which these generalise across brain areas and species. You will be working with an interdisciplinary team led by Prof Andrew Jackson funded by the Advanced
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materials to enable precise and reliable detection of key TLS-related biomarkers using robust, user-friendly electrochemical methods. Early detection and timely intervention of TLS will prevent organ
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and presents the opportunity to investigate semiconductors, composites, waveguides and resonators. The student will explore synthesis and fabrication strategies and develop methods for advanced optical
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resonators. The student will explore synthesis and fabrication strategies and develop methods for advanced optical characterisation. The student will be provided training and access to advanced
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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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and adapting existing simulation models of transport systems and development of methods for resilience analysis. The PhD project will suit students from any quantified background, including engineering