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products play a vital role by enabling cleaning at lower temperatures and with less water. A key component in these products are polymer additives, which enhance cleaning performance by aiding in soil
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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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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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. Kavanagh Eligibility Criteria A 2:1 honours degree, or international equivalent, in a relevant subject. A Masters with strong research training element would be highly advantageous. This funding is designed
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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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applications in economics using applied econometric methods, with an interest in the health and well-being benefits of arts engagement. Arts engagement can be broadly defined to include various forms of arts