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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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and planning algorithm for high-speed autonomous vehicle. The work will involve algorithm development, simulation, test rig set-up, and experimental validation. Requirements: The candidate must at least
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. The successful applicant will use state of the art inference algorithms to design, use and share the findings of epidemiological models that integrate across large and diverse datasets including capture-mark
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theoretical computer science to develop algorithms and/or data structures, to further our understanding of what is possible in various computation models. The Research Associate/Research Fellow is also expected
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the following tasks: Development of new large language models for process modeling Development of new decision-making algorithms Provide regular project updates to principal investigator and funding agency Report
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responsibilities Research: Contribute to or lead on the statistical aspects of the development of high quality research bids to evaluate the effectiveness of new health technologies, which is recognised both
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Responsibilities Development of new machine learning modeling approaches Development of new advanced control and optimization algorithms Optimization of carbon capture process operation Provide regular project
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extraordinary ideas - and the people who discover them The Opportunity We are seeking a highly motivated Research Fellow to join the Faculty of Science and School of Physics and Astronomy to develop methods
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based at the School of Electronics and Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing
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a variety of spectrum sharing scenarios, seeking opportunities for algorithms which provide enhanced interference resilience against different interfering systems. Develop, with colleagues, a spectrum