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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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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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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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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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(SDR) platforms and characterise them in the presence of interference in a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience
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combining these to explore the possibility of improving outcomes. These algorithms will then be used to develop a prognosis platform. You will investigate different approaches and find novel ways to improve
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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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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
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with Tokamak Energy but also with several prestigious research institutions worldwide, including Forschungszentrum Jülich in Germany, the French National Centre for Scientific Research (CNRS) in Orsay
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and refine algorithms and models for large-scale language processing tasks, with a focus on healthcare data Contribute to developing new models, techniques and methods for clinical machine learning