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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming
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communication skills. First-author publications at NeurIPS, ICLR, ICML, AAAI, KDD, or IJCAI. Experience working with large-scale, noisy, or real-world datasets. Experience with GPU-based training and high-performance
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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 with graph-based data analysis or anomaly detection methods. Exposure to high-performance or GPU-based computing environments. Demonstrated ability to contribute to publications or technical reports
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tracking), dataset curation, HPC/GPU programming, blockchain for secure data, C-family languages, and embodied AI/robotics are a plus. Experience with general network resilience, cellular automata
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
. The postdoctoral scholar will be expected to improve on existing GPU-accelerated ocean models and develop laboratory experiments (in the Joint Fluids Lab at UNC), analyze results, publish in peer-reviewed journals
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contact, as identified by AFRL through recent past efforts. This includes the implementation of relevant algorithms and solvers for distributed GPU computing within the JAX Python library. Qualifications
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with GPU-accelerated computation and high-dimensional data analysis. Enthusiasm for applying AI innovations to real biological and medical challenges. Required Application Materials: Cover letter
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
of data scientists/clinicians and working with unique datasets from multiple academic medical centers (e.g. UNC, UCSF, Mayo Clinic, Memorial Sloan Kettering, etc). Lab dedicated GPU workstations/servers and