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systems, ensure flow of multiple projects in parallel, data collection and storage. Reviews and tests robotics operations to define and correct errors and improve the output. Uses AI-based algorithms
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 1 month ago
of this project is to develop advanced numerical strategies for passive imaging of the Earth and the Sun. The position focuses on enhancing the capabilities of our massively parallel software, Hawen (https
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applications; initiates and leads research and development in the areas of data management systems for high performance computing (HPC), parallel computing systems; collaborates with faculty, research staff
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | about 1 month ago
supercomputers. IT4Innovations also operates the first Czech quantum computer named VLQ. For more details, see www.it4i.eu . Activity description: · development of methods for acceleration of parallel applications
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description: The Scientific Computing Center is the Information Technology Center of KIT. The Research Group Exascale Algorithm Engineering of SCC works at the interface of algorithmics, parallel computing, and
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be controlled, measured, and related to the thermal and mechanical properties of the foams. Several formulations and systems will be studied in parallel to shed light on these three issues
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Fall 2026. Knowledge of parallel programming and experience developing methods for 2D and 3D problems are critical. Experience working with open source software frameworks and/or using modern open
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infections. In addition, recent advances in massively parallel cell tracking now make it possible to address long-standing questions about how bacteria have adapted to survive in unpredictable environments
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/C++, and Python. Working knowledge for parallel programming models (MPI, OpenMP, CUDA, SYCL) Proven track record in GPU driver development, performance profiling, and debugging. Familiarity with Linux
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programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Serial and parallel debugging and profiling Parallel numerical algorithms and libraries. System software stack