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Field
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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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analytics, anomaly detection, and embedded redundancy to enhance system resilience. Students will focus on creating adaptive algorithms and hardware implementations that enable real-time diagnostics and
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. You will focus on developing microwave techniques and associated electronics to precisely control the curing process, using AI-based algorithms to optimise outcomes. Full support will be provided
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of rail with wider city and regional transport networks. A focus of this work is the application of optimisation techniques (e.g. evolutionary algorithms, or Bayesian techniques) to identify high performing
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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. - Collaborating with interdisciplinary teams to design and implement innovative solutions in MLOps. - Developing and optimizing algorithms for model compression and efficiency improvement. - Staying abreast
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to apply it in selected poor-resource settings. This project aims to achieve several objectives, including the development of a new AI-algorithm and a paired dataset for comparing how different imaging
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socioeconomic impacts of artificial intelligence (AI), gender bias in algorithms, critical analyses of fintech, dynamics of platformisation, the emergence of digitally enabled forced labour, biometrics and
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participate in developing algorithms for tau lepton identification, and will also have the opportunity to assist with silicon module construction for the ATLAS tracker upgrade. Instructions for applying can be
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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake