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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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incorporating time-dependent source depletion. (4) Reducing uncertainty in groundwater risk assessments through refined numerical methods. (5) Applying the improved model to real-world groundwater contamination
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with knowledge and interest in structural dynamics, finite element analysis, programming and numerical methods. Applicants are expected to have achieved or be about to achieve a First-class honours MEng
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FTE, 30 hours per week minimum). About you You’ll have a PhD (or equivalent experience) in a numerically focused field and a strong background in epidemiological and statistical methods. You’ll bring a
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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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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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. The student through participation in team meetings and training with the team will deploy a numerical model (4SAIL) to study the combined effects of the vegetation-soil system temperature and canopy reflectance
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This PhD project will focus on developing AI-based methods to accelerate the Swansea University in-house discontinuous Galerkin (DG) finite element solver for the Boltzmann-BGK (BBGK) equation
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-dependent source depletion. Reducing uncertainty in groundwater risk assessments through refined numerical methods. Applying the improved model to real-world groundwater contamination case studies. Career