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Field
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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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to significant losses during processing, transport, retail and storage stages. In parallel, advances in sensor, information and communication technologies, together with increased computational capabilities, have
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of analytical reports to achieve client, program, and business objectives for resource optimization. Work closely with internal technology teams and vendors to deliver tools and system solutions. Lead the
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.) Experience with big data tools (e.g., Hadoop, Spark, Kafka, etc.), data pipelines, and software development frameworks Experience with parallel programming (High Performance Computing experience is a plus) and
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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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Competitiveness and is organised into various divisions and services in support of ESA’s programme directorates. You will support the tender actions and resulting contracts/cooperative agreements assigned to you
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organizational skills and the ability to manage multiple hardware efforts in parallel. Prior experience working on spaceflight, high-altitude, or other high-reliability platforms. Experience working on-site with a
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | about 2 months ago
Python, e.g., Ray, Dask); Very good knowledge of hardware and software technologies in the areas of distributed computing and Big Data; Experience with container management tools (Docker, Singularity) and
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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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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