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recommended. Other valuable skills include: Experience in high-performance computing using MPI. Experience in GPU programming using OpenACC, CUDA, CUDA-Fortran, Julia, or related tools. Experience in CFD
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secretion systems (T4SS). The laboratory provides all the equipment required for the project, including standard microbiology facilities (L1 and L2) and biochemistry equipment (AKTA pure), GPU computing
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in rivers or estuaries with applications to plastic transport. The transport models will be included in existing shallow-water solvers running in hybrid CPU-GPU architectures. The expected workplan
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, as is experience with scientific, numerical, and/or GPU programming; • Have some prior experience with data science and/or machine learning; • Hold values such as honesty, modesty, collectivism
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degree in the above mentioned or related fields. What we offer State of the art on-site high performance/GPU compute facilities A team of 30+ expert colleagues A family friendly, green campus with on-site
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6G testbeds (indoor and outdoor) with GPU clusters and edge computing platforms Global Internet measurement infrastructure and satellite network access Opportunities to engage with Internet
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distributed computing for EMT simulations. • Experience with software development in Python, C++, or other programming languages. • Familiarity with GPU acceleration of numerical solvers, parallel sparse
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and high flexibility in where and when you work. Access to HPC resources (including GPU clusters) at Helmholtz, the Leibniz Supercomputing Centre (LRZ), and the Forschungszentrum Jülich (FZJ). Training
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on materials science tasks as well as integrate your semantic-AI services into high-throughput GPU/HPC workflows, contributing to data management, metadata structuring, and semantic annotation Collaborate with
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different hardware backends. Design conventional (GPU-based) deep neural networks for comparison. Publish research articles, regular participation in top international conferences to present your work