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of Sheffield is involved in the DUNE project with a broad range of responsibilities in detector construction, modelling and software development. This PhD project will focus on development of a methodology for
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maintenance, production efficiency, and quality control. While the benefits of ML are significant, its adoption also introduces risks such as data privacy concerns, algorithmic bias, model transparency issues
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. Philipp Petersen, M.Sc.. The research areas developed by the team are in particular related to theoretical analysis of classical problems in numerical analysis in the framework of modern algorithms
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explore data-driven methods including machine learning (ML) and artificial intelligence (AI) techniques, to develop predictive HMPM tools that can diagnose, detect, and predict faults in machinery
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electromagnetic design. We will explore advanced topologies for mmwave metasurfaces, design novel reconfiguration mechanisms, and develop intelligent algorithms to optimize scattering characteristics in real-time
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and Algorithms Ethics and Responsible AI The post holder will be expected to lecture on modules and programmes at undergraduate and postgraduate levels and take on programme leadership duties (at Senior
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Deadline: 31 October 2025 Details This project aims to develop new algorithms for reinforcement learning from human feedback, to effectively solve complex reinforcement learning tasks without a predefined
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, including how to guarantee the properties of stability and constraint satisfaction while probing the system and learning a new model. This project aims to develop novel algorithms for the adaptive distributed
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from such machines to derive algorithms expressing their state of health and next maintenance needs. A background in both engineering and machine learning would be useful, although help is readily
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undertake detailed design at all levels to cover the range of BEMS as operated by the University. • Highly developed and demonstrated consultative, interpersonal and communication skills • Evidence of