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of GPUs and/or time in either training or inference procedures, which pose considerable challenges to both academia and industry for widespread access and deployment. In particular, the sampling process of
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. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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nature of electroweak symmetry breaking and mass generation in the standard model. We developed state of the art (open source) software working on GPU- and CPU-based supercomputing architectures, and
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-scale training of a high-performance foundation model using a dedicated GPU cluster Fine-tuning the pretrained model on real-world health data from lifespin’s proprietary database Collaborating closely
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analysed in a standard web browser. HiPIMS computations can be executed on local GPUs or through cloud-based GPU services, empowering users to conduct large-scale fast flood simulations without worrying
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physics, mathematics or any related field. What we offer State of the art on-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A wide range of offers
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GPU NVIDIA H100. Interdisciplinary collaborations with AI researchers and clinicians from NHS hospitals. Participate in large collaborative project funded by the National Institute of Health and Care
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equivalent degree in physics, mathematics or any related field. What we offer State of the art on-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 2 months ago
-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A wide range of offers to help you balance work and family life Further training opportunities
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for collaboration inside and outside of the University. It has access to extensive dedicated computing resources (GPU, large storage). The successful applicant will work under the supervision of Prof. Hain. Please