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PhD project: Modelling Resilience of Water Distribution Networks Supervised by Rasa Remenyte-Prescott (Faculty of Engineering) Aim: To develop an modelling approach for assessing water network
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. Experience conducting systematic literature reviews or meta-analyses to inform model assumptions would be advantageous. We are seeking a self-motivated researcher with a PhD (or near completion) in Building
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-world interventions. A key part of this role will be performing systematic literature searches and meta-analyses of dose–response and exposure–response data to support model calibration and uncertainty
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searches and meta-analyses of dose–response and exposure–response data to support model calibration and uncertainty analysis. This role offers a rare opportunity to bridge public health and building science
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clinical trials within the unit. You will also be able explore a wide range of research skills including human volunteer and clinical stroke studies, data analysis including systematic review and meta
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. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Funding and eligibility 3-year, full-time PhD
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. Expect close collaboration with industrial experts and the opportunity to see your algorithms influence aerospace and other high-value manufacturing sectors. Funding and eligibility 3-year, full-time PhD
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studies, data analysis including systematic review and meta-analysis, scientific writing (abstracts and academic papers) and preparing grant applications for future studies. You will experience clinical
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, memory, and energy requirements. The successful candidate will explore novel algorithms and model-design strategies that allow AI systems to operate effectively on edge devices, clinical environments
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related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm and experience in using these algorithm for electrical engineering problems