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respectively, we seek highly motivated candidates to develop innovative methodologies for radar sounder data processing, with a strong emphasis on artificial intelligence and deep learning. The research activity
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to establish improved, AI-enhanced calibration strategies for diverse industrial and geophysical materials. Ultimately, it seeks to determine the optimal, AI-informed approach for selecting and calibrating
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to learn are essential. Essential: A first-class undergraduate degree or a master’s degree in Physics, Applied Physics, Electrical and Electronic Engineering, Mathematical Sciences, or a closely related
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components might be only practically verified using one verification method. For example, a machine learning vision component cannot be realistically formally verified but it can undergo a rigorous testing
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would be helpful, but these are not essential. The project is well suited to candidates from earth science, geography, engineering, or related backgrounds who are motivated to learn new technical skills
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) are widely used in industries such as automotive and electrical engineering. During service, these materials are frequently exposed to harsh environments involving elevated temperatures, oxygen, and moisture
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inspection technologies with techno-economic modelling to enable condition-based circular economy decision-making for offshore energy infrastructure. The project will: (1) evaluate autonomous inspection
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PhD in Informatics for Vibration Risk and Habitability Resilience in Sustainable Building Design Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26
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for MPhil). The duration includes an extension of up to 6 months (PhD) or 3 months (MPhil) if approved for your candidature. This is the full-time, tax-exempt rate which will index annually. You will receive
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep