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Field
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significant computational component in deploying multi-GPU codes to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest. Your contributions would
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cluster consisting of 16,616 CPU cores and approximately 100 GPUs, as well as a dedicated AI cluster with 40 A100/H100 GPUs. Position Overview The Department Chair will serve as the academic and
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with excellent facilities for protein science research. There will be direct access to advanced biophysical infrastructure in the biophysics core facility headed by the PI, a GPU cluster with working
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maintenance. Proficiency in programming languages such as Python, R, C#, and SQL. Familiarity with high-performance computing, cloud computing (e.g., AWS), GPUs, and DevOps tools, including version control
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. Key attractions are access to a high-performance computing cluster (GPU/CPU and more than 300TB of data), two 3T Prisma MR scanners, and an MR compatible digital EEG system as well as collaboration
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CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure platform management/partitioning
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(Xilinx Vitis/Vivado, Intel Quartus, HLS tools) HPC environments or GPU-accelerated computing On-detector firmware or data acquisition systems Familiarity with HEP data formats and reconstruction
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: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral
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to push the boundaries of what’s possible. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. We don’t
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machine learning techniques, and GPU programming. The simulation results will be compared to observational data obtained using facilities worldwide including ESO and NOT. Who we are looking for A successful