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mixed research methods—including behavioural surveys, environmental monitoring, and dynamic thermal modelling—the project aims to generate retrofit strategies that improve energy efficiency, reduce carbon
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examining the intersection of environmental challenges and the just transition. This role is ideal for candidates planning to apply to top PhD programmes and looking for hands-on research experience on high
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biology, and biochemistry. Plan, conduct, and contribute to experimental work. Identify and develop relevant methods for data collection, analysis, and interpretation. Develop expertise across scientific
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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analyse large, multidimensional 4D STEM datasets. Develop or adapt software tools (e.g. Python, MATLAB) for image reconstruction, phase mapping, and quantitative analysis of ferroelectric domain wall
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Location: South Kensington About the role: The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple
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technological change. This role is ideal for candidates planning to apply to top PhD programmes and looking for hands-on research experience on high-impact questions in environmental economics and innovation. Key
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Applications are invited for a PhD studentship in the Department of Computer Science at City, University of London. The successful candidate will work on developing a novel AI-powered conversational
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-subsaturated air (but where no contrail was formed). Research Associate: Hold a PhD in Aerospace Engineering, Atmospheric Science, Physics, or a closely related discipline*, or equivalent research, industrial
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to discuss the post informally, please contact Dr Yuanchang Liu () About you The ideal candidate will hold a PhD (or be close to completion) in Mechanical Engineering, Robotics, Marine Engineering, or a