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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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, with PyTorch and/or other GPU programming tools is also necessary. You should have completed all requirements for your PhD by the time you are hired. How to Apply: Candidates who have most, but not all
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Boltzmann method (LBM), discrete Boltzmann method (DBM), and other kinetic models. Solid knowledge of fluid dynamics and heat transfer. Proficiency in programming languages such as C/C++, Fortran, Python
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CADMY is offering several Postdoctoral Associate positions designed for recent graduates of Ph.D. programs working at the intersection of AI, Game Theory, Economics, and Computer Science. We
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. First, efficient and scalable training procedure are still needed, irrespective of whether the training is done off-line on a traditional GPU-based architecture, on neuromorphic hardware. Second
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experimental data. Experience in GPU programming. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is
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. Preferred Qualifications Experience with: C/C++, Python, MATLAB, ROS 1 and 2, OpenCV, Unity, GPU programming, linear and nonlinear control theory, supervised, unsupervised and reinforcement learning, Torch
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
workloads with dedicated GPU and large-memory partitions. The Research Triangle area is a dynamic collaborative environment with UNC-Chapel Hill, Duke University, and North Carolina State University all
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
, including the 17,000-core Longleaf cluster optimized for I/O intensive workloads with dedicated GPU and large-memory partitions. The Research Triangle area is a dynamic collaborative environment with UNC
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conferences. Qualifications: PhD in computer science with file systems, GPU architecture experience. Proven ability to articulate research work and findings in peer-reviewed proceedings. Knowledge of systems