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Computer Science, Southampton. The project is researching, developing and evaluating decentralised algorithms, meta-information data structures and indexing techniques to enable large-scale data search
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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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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous
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multi-agent autonomous systems and related technologies. This will include development of distributed monitoring algorithms enabling agents in a multi-agent swarm to autonomously locate other agents in
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validate these distributed intelligence algorithms, enabling breakthroughs in scientific research across DOE domains. The candidate will collaborate with DOE’s SWARM project (https://swarm-workflows.org
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to engage in pioneering research, collaborate with a large, dynamic and multidisciplinary team, and advance the field of quantum computing through innovative algorithms and technologies. This is an exciting
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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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project aims to address the current limitations of traditional frame-based sensors and associated processing pipelines with a new family of algorithmic architectures that mimic more closely the behaviours