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- into a GPU-enabled and parallel code to run efficiently on state-of-the-art exascale hardware Designing implementations and reviewing community contributions of library features and new statistical
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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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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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, we seek a highly motivated researcher with a proven track record in parallel programming models for CPS, high-level compilers, system and computer architecture and automatic code optimization
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the REACT Doctoral Network. In this context, we seek a highly motivated researcher with a proven track record in parallel programming models for CPS, high-level compilers, system and computer architecture and
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Since 2020, Fraunhofer Heinrich Hertz Institute has worked with United Nations
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opportunity to develop and lead their own independent research project, recruit PhD students through the IMPRS PhD program, and build a team by hiring postdoctoral researchers. In parallel, they will oversee
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Max Planck Institute of Immunobiology and Epigenetics, Freiburg | Freiburg im Breisgau, Baden W rttemberg | Germany | about 2 months ago
independent research project, recruit PhD students through the IMPRS PhD program, and build a team by hiring postdoctoral researchers. In parallel, they will oversee the development and implementation
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Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 3 months ago
. Degree in Computer Science, IT, or a related field, or equivalent experience. Preferred: Familiarity with parallel computing, job schedulers, and high-speed networking. Experience with storage technologies
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, parallel/distributed computing, as well as diverse architectures and understanding of its impact on application performance Knowledge in GPU-based programming and modelling of scientific simulations