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partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML Enhancement of AI/ML with in-network computing & processing Adaptation & optimization
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We are looking for a highly motivated candidate to pursue a PhD programme titled "CFD-informed finite element analysis for thermal control in wire-arc directed energy deposition." This research
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vibration isolation). In parallel to the benefits of quantum computing, artificial intelligence and neuromorphic computing seek to emulate the massively parallel, highly efficient computing capacity
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, Statistical Physics, Genome Annotation, and/or related fields Practical experience with High Performance Computing Systems as well as parallel/distributed programming Very good command of written and spoken
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position within a Research Infrastructure? No Offer Description Project description Third-cycle subject: Computer Science This Ph.D. project will develop fundamental theory and methods in distributed systems
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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including workload schedulers, storage systems, and distributed compute nodes. Applies analytical methods to evaluate system performance, identify bottlenecks, and implement corrective actions to improve
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of computational computing clusters including, but not limited to GPU clusters, General HPC clusters, and SMP environments. Experience with Centos or similar Linux distributions. Experience with programming jobs
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | about 1 month ago
(supercomputer/cloud/edge) infrastructures. Developed by the KerData research team in the context of the STEEL project of the national PEPR CLOUD program, E2Clab (https://team.inria.fr/kerdata/e2clab/ ) is a
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problems in science and engineering on high-end parallel and distributed computing platforms. The employment duration is 3 years. Initially limited to 1.5 years, the employment relationship is automatically