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variety of computational devices (e.g. CPUs and GPUs) while ensuring overall consistency and performance. - contribute to identify new CSE applications domains, such as condensed matter systems, quantum
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scientists and engineers are accustomed to. Moreover, the vast majority of the performance associated with these reduced precision formats resides on special hardware units such as tensor cores on NVIDIA GPUs
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E13) up to 5 years International collaboration to build a large radiotherapy dataset Dedicated GPU infrastructure Strong collaborations within TUM’s AI ecosystem High-impact publication potential
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 9 hours ago
. 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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finite-element models, e.g. Poisson, linear elasticity, large-deformation soft tissue, for real-time execution on AR devices and GPUs Implement these models within open-source frameworks such as SOFA
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. In addition, CVI2 provides high-performance GPU computing resources that support the design and training of advanced AI models. The research agenda of CVI2 focuses on cutting-edge topics such as 3D
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clusters, cloud computing, or GPU acceleration. Strong mathematical background in linear algebra, probability, and statistics. Prior research experience with publications or preprints. The University
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models on GPU-based systems; familiarity with HPC environments is an advantage Interest in interdisciplinary research at the interface of AI and genomics; prior experience with biological data