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efficient inference. Experience with GPU computing and high-performance or distributed computing environments. Exposure to planning, scheduling, workflow engines, or reasoning systems, and to scientific
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substantial investments in on-premises GPU infrastructure that allows us to work with confidential partner data under full data control. The focus of the position is on research, aiming at publications in
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science, conversational AI, and marketing analytics and on substantial investments in on-premises GPU infrastructure that allows us to work with confidential partner data under full data control. The focus
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Python and experience with GPU processing of large-scale datasets. Excellent written and oral communication skills in English. We also value applications from people with the following experience
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scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D reconstruction methods for tomography, laminography, or a closely related
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Python and experience with GPU processing of large-scale datasets. Excellent written and oral communication skills in English. We also value applications from people with the following experience
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collaboratively in a research-focused environment. Project background We train open foundation models with hundreds of billions of parameters on thousands of GPUs on one of the largest AI-ready supercomputers in
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track record of open-source contributions. Project background We train open foundation models with hundreds of billions of parameters on thousands of GPUs on one of the largest AI-ready supercomputers in
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with thousands of CPUs and GPUs, petabytes of storage, and high-speed, low-latency networks across multiple data centres in Switzerland. As research workloads continue to evolve, we are building a hybrid
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object storage for the concurrency, small-file, and large-checkpoint patterns of distributed GPU training and HPC simulation. Operate at scale, safely. Design and run multi-petabyte storage with