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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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. 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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machine learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key
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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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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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analysis algorithms for the observation and interpretation of existing and new spectroscopic data of exoplanet atmospheres. Experience on cloud/haze microphysics modelling and large scale simulations is
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small highly motivated inter-disciplinary team working towards a shared goal. You will be responsible for the design and testing of original machine-learning based algorithms and models for multi-modal
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, estimation, and identification algorithms that directly interface with physical hardware. We work closely with industry partners. Our research has led to several methods now used in commercial products. We
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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot