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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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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month 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 1 month 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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days 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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days 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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. 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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, 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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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
, or related fields Strong programming skills in Python, R, and experience with Linux environments Demonstrated experience in processing and analyzing large-scale genomic and transcriptomic datasets
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performance computing numerical methods in our state-of-the-art open source micromagnetic model, MagTense. MagTense is based on a core implemented in the Fortran programming language, and it relies