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Field
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scientific data in a storage/data lake. Moreover an interface between computing resources and distributed data storage needs to be created. In the context of this position an efficient data organization and
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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Announcement for fields of study such as: Automation technology, software engineering, computer engineering or comparable. Fraunhofer IPA is an institute for Manufacturing Engineering and Automation
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. Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion?set_language=en) Candidates may apply to multiple positions offered
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Hours Per Week 40 Offer Starting Date 1 May 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Marie Curie Grant Agreement Number 101227277 Is the Job related to
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on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training and optimizing the execution User support in
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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Neckarsulm, aiming to explore and develop AI algorithms, frameworks, and hardware architectures for efficient edge deployment in vehicles, with a focus on neuromorphic computing. @Fakultät@-Plakat-A4-hoch
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, reproducible computational workflows for large-scale screening and multiscale model integration. HPC at Scale: Run, optimize, and profile large simulations on HPC systems; ensure efficient parallel performance