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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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, 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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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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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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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
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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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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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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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for projects related to the integration of microgrids, distributed energy resources (DERs), and advanced transmission & distribution (T&D) coordination. This role is ideal for a researcher passionate about
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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes