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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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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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, parallel processing and hardware-oriented applications and optimisations as well as operating systems. The project will be carried out in close cooperation with Audi in Ingolstadt and Neckarsulm, aiming
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processes under ultra-high vacuum and chemically reactive environments. This predictive framework will enable rational design of vapor-phase synthesis pathways and support close collaboration with
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invasive sensing tools to monitor metabolites, oxygen, carbon dioxide, pH, and other parameters. Ideally, the methods can function in parallel and on a large scale. The research is vital to understand key
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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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process, the role of ISOs passing through molecular clouds, taking part in molecular cloud collapse and disc formation. Your tasks in detail: Perform scientific work on the research topic, in collaboration
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. We advance the understanding of dynamic processes to address global challenges, from mitigating the impacts of natural hazards and sustaining our habitat amid global change to responsibly managing
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. The field has emerged in parallel with rapid technological improvements in computing, the spread of Internet and mobile technologies, and the increased digitalization of data and of people’s lives. Our group