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, economic viability, and robustness to realistic operational uncertainty. PhD (or equivalent) in control engineering or closely related discipline. Track record in at least two areas: model predictive control
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, you will explore water-gas shift chemistry to tailor sustainable carbon dioxide and hydrogen concentrations as the first step to produce sustainable fuels. During the project you will design manufacture
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Cranfield University's tailored researcher training programme – setting you up for a rewarding career in water science and environmental technology. Per- and polyfluoroalkyl substances (PFAS), also known as
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significantly lower than that. Smart meter technology enables, potentially, a good understanding of domestic heat demand. However, these demand profiles are influenced by the technology of heat delivery, which is
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Applications are invited for three (3) PhD Studentships, based at the Department of Statistical Science, UCL. The positions can involve any topic within the Statistical Science remit. The positions
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should be submitted via the Engineering Materials PhD programme page. In place of a research proposal, you should upload a document stating the title of the project that you wish to apply for and the name
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of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading experts in machine learning for healthcare. You will also be affiliated to the School of Health
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
engineering and nanotechnology, and it will require the realization of novel devices for wide range of applications such as photodetectors, optical modulators, optical amplifiers, lasers, biosensors etc. based
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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of Europe’s leading academic departments of population health sciences. The post will suit researchers interested in understanding computers energy usage to improve carbon footprint monitoring and reduce the