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Overview The University of Sheffield has a large and varied portfolio of buildings which require careful management in terms of the water services. The Water Compliance Team works together
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limited to machine learning, Natural Language Processing, large language models, data visualisations, and linked open data, can help streamline and improve editorial workflows. At the same time, it
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, and spray-on electronics, working with several industrial collaborators, including Tata Steel, Rolls-Royce and the National Nuclear Laboratory. The candidate will benefit from working within a large
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low carbon district heating as a key strategy to enable large scale decarbonisation building sector, to achieve net zero by 2050. Currently, 2- 3% of heat demand in the UK is covered by district heating
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. Cyber-physical power systems deploy the latest information and communications technologies to enable the data and information flows across different entitles of physical networks. Such ‘cyber’ systems
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resolution. Using these data, the student will build and calibrate numerical models to simulate conservative transport and mixing. A combination of particle-tracking and grid-based reactive transport models
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further information about the group’s work and publications at http://osbornelab.group.shef.ac.uk. Enquiries should be directed to Colin Osborne c.p.osborne@sheffield.ac.uk. Please apply for this project
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the UK transitions to net zero, a large number of homes will have to be retrofitted to improve energy efficiency. This presents both a risk and an opportunity for improving indoor air quality, making it
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requiring significantly less interactive human feedback than current RLHF methods. To achieve this, the project will focus on extracting more information from uncertain, incorrect, and inconsistent human
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the losses out of the chip are negligible. In this limit, we can use the quantum dot to impart a large phase shift to the photon. Depending on the position of the quantum dot, the phase shift can be linked