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Field
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vendors, each with their own management interfaces, data formats, and access restrictions. In parallel, AI-driven services are increasingly deployed in a distributed fashion, with inference spread across
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Commissariat à l'Energie Atomique et aux Energies Alternatives - Groupe | Gif sur Yvette, le de France | France | 3 months ago
/1.5128502 [Hamouda2022] W. Hamouda et al., Appl. Phys. Lett. 120, 202902 (2022) https://doi.org/10.1063/5.0093125 [Iung2025] T. Iung et al., Oxygen vacancy distribution and phase composition in scaled
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Research Framework Programme? Not funded by a EU programme Reference Number COMPETE2030-FEDER-00858000 Is the Job related to staff position within a Research Infrastructure? No Offer Description NOTICE
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Education and Experience Preferred Qualifications: PhD in Computer Science, engineering, science, Mathematics, Data Science or similar quantitative subject areas. Expert knowledge of HPC systems, best
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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software for multi-arch environments Development in high-performance computing (HPC) or distributed systems Strong understanding of Linux toolchains, build systems (CMake), and debugging tools Parallel
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- Posting may close at any time) Job Summary We are looking for MS and PhD students with experience and interest in state-of-the-art data management technologies, high performance computing, memory management
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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers