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You will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form
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with a surface. This project will involve using and further developing both the experimental and data analysis methods that are currently used within the research team. The student will learn how to use
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this astonishing picometre fabrication precision. Further aims of the project include: Theoretical modelling of nanoscale effects and processes in SNAP Development of experimental methods of picometre-precise
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in a degree, ideally at Masters level, in an Engineering subject, Physics, Mathematics, Computer Science or other quantitative background. Knowledge in fluid mechanics, ocean waves, numerical methods
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sensitive to malicious deviations while remaining resource efficient. Solutions must operate effectively on network gateways or even capable IoT devices. The research will investigate statistical methods
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, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace
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Involvement in the preparation of articles for publication in scientific journal(s) Good numerical and statistics skills and familiarity with text editing software, such as Word, Excel, etc. Knowledge
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experimental methods to improve them, alongside developing new approaches to enhance these properties. External environmental factors, such as humidity, pressure, and temperature, will also be key variables
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alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic