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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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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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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
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System applications, system/segment/payload system architectures and performance, signal-in-space and signal processing, GNSS receivers, algorithms, OD&TS, formation-flying and radio frequency/optical
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PhD Stipends within Distributed, Embedded and Intelligent Systems (DEIS) At the Technical Faculty of IT and Design, Department of Computer Science, one PhD stipend is available within
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. This position offers an exciting opportunity to work at the intersection of HPC and AI, addressing critical communication bottlenecks and optimizing network interconnects for large-scale distributed systems
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scientists, external Assessment scientists and management personnel as needed and/or requested. Liaise and coordinate with the science team to ensure that the algorithms, procedures, mathematics, and