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Field
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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the requirements of distributed LLM inference at collaborative edge environment; (b) design the system model of collaborative edge AI for distributed LLM inference; (c) design algorithms and
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, encryption/decryption and compression; use of microelectronics devices (including COTS); implementation, inference, verification and validation of algorithms** on processing hardware platforms for space
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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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NSF funded projects, advancing the knowledge about distributed systems, developing novel algorithms for distributed resource and workload management, simulating and emulating systems, as
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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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the emergence of edge computing, data storage will become more geo-distributed to account for performance or regulatory constraints. One challenge is to maintain an up-to-date view of available content in such a
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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 job involves developing algorithms for embedded systems that are designed to produce sensing and computation on the image plane, and on understanding the best ways to distribute visual computation along
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on developing advanced new algorithms, testing and validation, and applications in medical neuroimaging and non-imaging modalities. The candidate will contribute to the overall research goals and objectives