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Deadline 19 Jan 2038 - 03:14 (UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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and optimize large-scale training and inference runs for foundation models on JUPITER (multi-GPU/node, mixed precision, parallelization, I/O optimization) Integrate multimodal data sources (e.g., scRNA
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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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, 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
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structured program. The projects address the three “One Health” aspects: 1) human roundworm infections (close cooperation and fieldwork in Kenya/Africa), 2) roundworm infections in farm animals (chickens and
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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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and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be considered an asset Proven record in publication
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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
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and/or Matlab, parallel programming Experience in international collaboration Fluent in English (spoken and written) Demonstrated ability to publish in international journals and present at conferences