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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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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | about 5 hours ago
powerful MPA-owned parallel computing clusters. It has also privileged access to supercomputers at the Max Planck Computing and Data Facility. Interested scientists are invited to apply electronically
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Max Planck Institute for Evolutionary Biology, Plön | Plon, Schleswig Holstein | Germany | about 5 hours ago
on several available Drosophila panels. In a parallel evolve and re-sequence (E&R) project, Drosophila populations will be experimentally evolved for larger/smaller embryo size. The postdoc will be responsible
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, Statistical Physics, Genome Annotation, and/or related fields Practical experience with High Performance Computing Systems as well as parallel/distributed programming Very good command of written and spoken
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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific
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engineering Very strong mathematical and algorithmic background Programming experience (Python, C++, etc.) Familiarity with parallel programming frameworks (e.g. MPI, CUDA) Fluent in written and spoken English
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high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel computing. Together with our existing rich dataset, we will inform a soil-plant digital twin, enabling
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parallel, they will oversee the development and implementation of computational strategies to support and enhance research activities across the institute. Your Responsibilities: Lead and further develop
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and postdocs. In parallel, they will oversee the development and implementation of computational strategies to support and enhance research activities across the institute. Your Responsibilities: Lead
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training and inference of GMMs for large, high-dimensional datasets Explore parallelization strategies to leverage modern GPU architectures Benchmark GPU-based implementations against CPU-based approaches