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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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developing new methods and techniques that will improve standard ML algorithms so as to achieve good performance outside their training distribution, by treating high-dimensional problems as an explicit
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and its implementation in distributed systems. Main responsibilities: Research, develop, and optimise machine learning algorithms, including deep learning, for AV control and coordination. Apply multi
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, the post-doctoral fellow will consider designing distributed learning algorithms for streaming manifold-valued data. Experiments will be carried out on urban, coastal, and underwater DAS data. The novelty
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frameworks, as they impose minimal, if any, assumptions about the underlying data distribution, making them more effective for detecting a wide range of changes. The CPD algorithms will be designed
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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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theoretical physics, whose responsibilities relate to distributed systems and the GPU optimization of AI algorithms. We expect the team to grow in size considerably over the next few years, and are looking
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schools for the student records system. Resolves enrollment and scheduling algorithm discrepancies and provides reports and other information related to students and the academic programs of the university