309 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at University of Sheffield
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, liaising with them to ensure efficient and rigorous collection of field data. Applicants must have experience equivalent to Level 3 (BTEC/A Level) qualification in relevant field, with proven vocational
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Award at the University of Sheffield. Imagine being able to check that a powerful quantum computer has performed a calculation correctly without having to repeat the computation or learn the private data
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appropriate information and advice to callers, often helping determine who at the University callers need to liaise with. Using the University’s Finance systems and following budget approval, create and convert
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, financial administration tasks, and organising and supporting School events and meetings and keeping School information up to date. You will also play a key role in supporting senior staff in their work
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research, high-throughput data generation, and bioinformatics analyses, contributing to high-impact research at the interface of plant and fungal physiology, molecular biology, and ecology. You will have a
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using deep learning. These data-driven approaches have proven to be highly flexible and powerful, able to generate nonlinear control policies able to act on the nonlinear plasma dynamics by learning
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Application Deadline: Applications accepted all year round Details This PhD project focuses on using topological data analysis (TDA) in machine learning (ML) to study (exotic) quantum matter systems defined
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at the University of Sheffield. Single photons are the indispensable information carriers for core quantum technologies, including quantum communication and computation. The critical hurdle is the reliable
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engagement and involvement co-production methods (such as the development of accessible information and research methods, working in collaboration with partners, seeking their advice and acting on it and
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energy infrastructure is designed with a focus on efficiency and reliability under “normal” conditions. Traditional risk assessment methods look at historical data and isolated failure scenarios. But in a