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to begin September 1, 2025. We will consider strong candidates in any research area but will prioritize Distributed and Parallel Computing. A PhD in computer science or a related area is required
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this, these simulations need to be massively parallelized. The objective of this thesis is to implement and evaluate different contingency parallelization approaches using our group's computational infrastructure
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-performance computing, including parallel or GPU programming (MPI, OpenMP, CUDA, Kokkos, etc.) Familiarity with modern software development practices, including debugging, profiling, and version control
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Nature Careers | Port Saint Louis du Rhone, Provence Alpes Cote d Azur | France | about 13 hours ago
protein expression and purification, capable of producing thousands of proteins in parallel within weeks . 2) Eukaryotic expression systems facility for production of challenging protein targets. 3) A fully
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, calcium imaging, optogenetics and/or behavioural methods. The project is part of a broader research programme designed to use cross-species research to uncover mechanisms for memory in both health and
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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Computing (HPC) knowledge around cluster builds, software, parallel computing, workload management, and cluster management. Knowledge with CUDA Programming Workflows, GPU programming, and GPU support
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management of a state-of-the-art medical imaging research data center. The environment includes a robust multi-CPU/GPU architecture with virtual and physical servers, supporting advanced parallel computation
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You will be working in a larger research and development project in parallel, distributed and heterogeneous computing. The project work can, under certain circumstances, be combined with an internal
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-performance computing (HPC), parallel computing frameworks (such as MPI, OpenMP) and GPU acceleration. Proven experience administering, configuring, and optimising HPC clusters and GPU systems (e.g. CUDA