638 postdoc-in-thermal-network-of-the-physical-building PhD positions in United Kingdom
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Field
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can be adjusted upon agreement with the successful candidate). Project Overview The drive for net-zero and sustainable manufacturing is reshaping the future of advanced materials. Traditional composite
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Computational verification of high-speed multi-material flows, where physical experimentation is highly limited, is seen as critical by the Defence Sector (source: the UK Atomic Weapons
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will contribute to the field by: Developing a conversational AI interviewer capable of conducting real-time adaptive interviews. Building an automated candidate ranking model based on interview
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deployments. Candidate Requirements Applicants should have (or expect to receive) a UK 1st class, 2:1 or equivalent in electronic engineering, physics, or a closely related discipline. Experience with
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backgrounds to join our community. At the Department of Applied Physics, our pioneering research in physical sciences creates important industrial applications that hold great technological potential. Our
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Net Zero 2050 goals, electric motors must undergo a transformational leap—from today’s typical power densities of 2–5 kW/kg to a step-change 10–25 kW/kg by 2035. The highest power dense motors today
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models and physics-based models. More recently, hybrid prognostics approaches have been presented, attempting to leverage the advantages of combining the prognostics models in the aforementioned different
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The research in this doctoral opportunity will investigate the relationship between material elastic and thermal properties by using high resolution digital imaging under dynamic loads. Digital
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this, the impact of the heterogeneity in the structure of the individuals contact network on disease transmission will be investigated. The candidate will gain experience in a range of mathematical and computational
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. Research will be focused on the lifelong safety assurance of increasingly autonomous AI systems in dynamic and uncertain contexts. It will build on methodologies and concepts in disciplines spanning AI