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of Information Technology. This role oversees the full lifecycle of events, ensuring alignment with university priorities and exceptional execution. In parallel, the position drives creative branding and communications
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). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance portability programming language (e.g., CUDA, HIP, Kokkos, SYCL
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, science, engineering, and mathematics applications used in computational computing environments. Supports management, tuning, operating systems, parallel file systems, automating deployment of compute nodes
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) ● Experience working in Linux-based high-performance computing environments. ● Experience with parallel programming models, such as MPI, OpenMP, CUDA, or related technologies. ● Experience compiling
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environments, parallel and distributed file systems, tiered storage strategies, and hybrid cloud integration. Strong communication skills across technical and non-technical audiences Strong understanding
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, sequencing, molecular cloning and genetic engineering techniques, DNA fingerprinting, MLST typing etc. Whole genome sequencing using Illumina MiSeq or similar platform. Participating in parallel work on other
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IFREMER - Institut Français de Recherche pour l'Exploitation de la MER | Nantes, Pays de la Loire | France | 24 days ago
parallel, please submit your application to the doctoral school : 20/04/2026 Doctoral students' contracts will start as of October 5th, 2026, subject to the submission of administrative documents authorizing
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and place-based linkage. ● Experience working with large, complex datasets that may require novel data structures, indexing strategies, or parallelized workflows. ● Ability to work independently with
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will work in close synergy with Alessia Platania’s parallel research group at the Niels Bohr Institute, with regular joint meetings as well as research visits in both directions. The University of Graz
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structures under complex loading conditions. In parallel, AI-assisted strategies for model identification and reduction (e.g., physics-informed neural networks, hybrid models) will be explored to efficiently