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disease insights. The lab has state-of-the-art computing capabilities with an in-house cluster serving 80 CPU cores and 1.5TB of RAM, as well as a newly acquired NVIDIA DGX box with eight H100 GPUs and 224
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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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managing supercomputer resources Strong skills in algorithm development for large sparse matrices Excellency in programming GPU accelerators from all major vendors Very good command of written and spoken
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frameworks (preferably Pytorch) Use of Linux GPU servers via command line Written and spoken scientific English It would be a plus to have familiarity with: GIS and remote sensing Internal Application form(s
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programming (Shared and Distributed memory, GPU programming etc.) Demonstrated experience with distributed memory MPI programming Experience with collaborative software design, development, and testing
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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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mathematicians, and domain scientists Develop software that integrates machine learning and numerical techniques targeting heterogeneous architectures (GPUs and accelerators), including DOE leadership-class
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engineering. The work involves simulations for quantum error correction and mid-circuit operations, and will require both low-level optimization skills (e.g., SIMD, GPU, FPGA) and an understanding of quantum
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projects at CASS. The center fellows will have access to a 70,000-core Infiniband Cluster (Jubail) dedicated to the science division, several GPU-based clusters at NYUAD, and other supercomputer facilities
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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