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Field
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. In this role, you will be part of the research team, working to develop and evaluate privacy-preserved Generative AI algorithms for generating synthetic Personal Identity Information (PII). This aims
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only within dense, highly interacting systems, inaccessible to standard techniques. To probe such regimes requires the development of fast and scalable algorithms for many-component systems, and of
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) lead market analysis, requirements gathering, and conceptual design; (b) optimise electromagnetic design of motor through simulations; (c) develop control algorithms and models; (d) guide
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develop algorithms to identify and predict SRL subprocesses from multimodal learning data (e.g., EEG/fNIRS, eye-tracking, and think-aloud protocols); • Analyze large-scale learning analytics data
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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
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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decentralised algorithms, meta-information data structures and indexing techniques to enable large-scale data search across Personal Online Datastores (pods) hosted on distributed pod servers, addressing both
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position is part of the AI2 (Algorithmic Assurance and Insurance) research initiative, an ambitious programme supported by the UK Prosperity Research Scheme with partners from both industry and academia. Our
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for trust and authenticity, perceptions of AI and algorithms in digital information environments, news and technology in everyday life, differences in attitudes to AI between journalists and audiences
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algorithms in digital information environments, news and technology in everyday life, differences in attitudes to AI between journalists and audiences, or experiences and understandings amongst different