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given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel
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IFREMER - Institut Français de Recherche pour l'Exploitation de la MER | Brest, Bretagne | France | about 6 hours ago
Research Framework Programme? Not funded by a EU programme Reference Number 2026-1852/1 Is the Job related to staff position within a Research Infrastructure? No Offer Description Deadline for applications
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Qualifications Experience: Relevant programming experience developing, implementing, debugging, and maintaining applications with Python. Experience working with high performance computers (e.g., parallelizing and
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given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel
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physics Physics » Thermodynamics Computer science » Modelling tools Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Application Deadline 19 Apr 2026 - 23:59 (Europe/Brussels
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State University of New York University at Albany | Albany, New York | United States | about 10 hours ago
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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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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, for example: Large‑scale optimization and machine learning: Stochastic and/or (non‑)convex optimization methods, first‑order methods, variance reduction, distributed and parallel optimization, federated
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 3 months 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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including workload schedulers, storage systems, and distributed compute nodes. Applies analytical methods to evaluate system performance, identify bottlenecks, and implement corrective actions to improve