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(SRM) may be deployed to counteract harmful effects of climate change. However the impacts of SRM on (eg) monsoons, extreme weather, and air quality are poorly understood. We seek to develop and apply
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digital twins with quantified uncertainty. This project will develop a measurement-science-driven digital twin framework for energy assets, initially demonstrated on PV modules/fields and battery systems
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together to answer some of the most important questions about the causes, prevention and treatment of disease. We are seeking a Senior Researcher to take the lead in the development of statistical and
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this challenge by developing modern data driven tools that help predict and manage corrosion in next generation bio refineries. The project brings together leading researchers from University of Leeds
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correspondence. Together, these materials form an insect archive that reveals evolving relationships between people and insects. The student will develop their own research questions, with wide scope to choose
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studentship will broadly explore development of cutting-edge AI solutions for image registration, anatomy segmentation, and immersive technology. The selected candidate will work with preoperative MRI/CT scans
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Recent advances in artificial intelligence (AI) have fuelled the idea of building “AI scientists”, i.e., systems that can perform part or all of the research process on their own. The development
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The research in this doctoral opportunity will develop a failure model that can represent the combined effect of surface and bending failures in gears to perform reliable health prognostics. Lack
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placement within Siemens Digital Industry Software. Project Overview The project focuses on developing and integrating ML techniques to enhance wall treatments for under-resolved boundary layers in
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provide considerable training in chronobiology, signal transduction in plants, microbiology, and data analysis and interpretation. As part of the “MicroClock” programme funded through an ERC Synergy grant