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real-time, and will be tested and demonstrated on a state-of-the-art HiL rig and an autonomous test vehicle. The post is focused on the development of automotive-grade algorithms and estimators that will
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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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create knowledge. We are looking for an early career researcher or PhD student at their final stages to work with us in the UKRI CHAILD project. CHAILD is UKRI-funded project that aims to establish a
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to) fundamental research in machine learning or statistics, algorithm design, the application of AI methods in science, healthcare, social sciences, or business. You should have a PhD or equivalent level of
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model checkers; proofs of safety and/or security properties; programming languages and/or type systems; concurrent and/or distributed algorithms; and related topics. The successful applicant will work in
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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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beginning in this role. We will require all candidates invited to interview to apply for NSTIx clearance. In this role you will: Design and test detection and estimation algorithms utilising artificial
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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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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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10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre