16 Engineering-"https:"-"https:"-"https:"-"INL---INSA-Lyon" Postdoctoral positions at University of Liverpool
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The opportunity The University of Liverpool is a key partner in a £14 million initiative ( https://tinyurl.com/yc5z768m ) to develop a sustainable, next-generation manufacturing facility, using
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The opportunity The University of Liverpool is a key partner in a £14 million initiative (https://tinyurl.com/yc5z768m) to develop a sustainable, next-generation manufacturing facility, using
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novel plasma-disturbance sensing techniques. The role sits within the Department of Electrical Engineering and Electronics, part of the Faculty of Science and Engineering, which has a strong track record
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nature of the role, prior experience in working with rheodialysis technique is an essential requirement. The position will be hosted in the Department of Materials, Design and Manufacturing Engineering at
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of the project we welcome applications from candidates who hold a PhD in a range of fields including Physics, Chemistry, Materials Science, Engineering. To apply for the position please follow the apply link and
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A National Institute of Health and Care Research (NIHR) Health Technology Assessment funded Postdoctoral Research Associate (PDRA) position is available at the Department of Cardiovascular and
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chemistry or chemical engineering, a strong publication record, a pro-active approach and strong expertise in gas phase heterogeneous catalyst testing and in catalyst synthesis and characterisation. The post
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effects while building machine-learning-ready kinetic datasets for predictive catalyst design. You should have a PhD (or about to obtain) in Chemistry or field related to this project (Chemical Engineering
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of researchers to develop new heterogeneous catalysts for conversion of CO2 to light olefins using high-throughput experimental techniques and AI tools. You will have a PhD in chemistry or chemical engineering, a
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials