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Field
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be in developing new algorithmic techniques for testing and verifying highly distributed database systems. Start date: The starting date is 1 October 2025 or as soon as possible hereafter
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computational tools to support the safe and ethical deployment of AI in clinical settings. The research focus is on AI performance monitoring, distribution shift detection, bias assessment, and stress testing
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, coordination, and decision-making algorithms for multiple autonomous agents—such as robots (robotic manipulators, drones, or vehicles)—that work together to achieve common goals in dynamic, uncertain
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the distribution of mechanical loads in the wind farm, thus also extending the lifetime of the wind farm. Your PhD project is integrated into the Living Lab 70 GW Offshore Wind, which researches crucial aspects
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. Key Accountabilities • Design and develop embedded AI algorithms for appliance profiling using smart meter data • Benchmark performance against state-of-the-art NILM approaches using datasets like
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areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics. You will be creating data-driven algorithms which can solve state estimation problems in fluid
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-driven algorithms which can solve state estimation problems in fluid mechanics, such as inferring the instantaneous state of a fluid’s velocity field from sensors embedded in its boundary. The research
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our software development team, developing novel scientific algorithms and applications in the areas of spectroscopic analysis and mining of the science data catalogues extracted from the pipelines
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source tools for distributed biomanufacturing of enzymes and antibodies at low-cost using benchtop microbial and plant systems. The overall goals of OpenBioMAPS are to work with UK biomanufacturing
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data Development of algorithms for infection and evaluation of infection hotspots in the plant population Coordination of the scientific interface to the project partners with regard to entomological