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be an expert within object oriented fortran, CUDA, HIP, build systems such as CMAKE and Autotools, the Github CI/CD platform and schedulers such as SLURM and LSF10. The candidate should hold a MSc in
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CUDA and scientific computing libraries (e.g., NumPy, SciPy). Workload: Approximately 15 hours per week on average during the semester, with the possibility of increased working hours (up to 40 hours
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Slurm) and package managers (Spack or similar); and 12. Technologies for heterogeneous large-scale computing (CUDA-aware MPI). This position is eligible for LBNL's Employee Referral Program benefit(s
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. Experience with MatLab, CUDA and IDL programming preferred. About the Department School of Physics and Astronomy: https://cse.umn.edu/physics Pay and Benefits Pay Range: $15.25 - $20 per hour; depending
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-performance computing (HPC), parallel computing frameworks (such as MPI, OpenMP) and GPU acceleration. Proven experience administering, configuring, and optimising HPC clusters and GPU systems (e.g. CUDA
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, CUDA. B3 Knowledge of UK data protection laws and certification and accreditation schemes related to the processing of sensitive data for research. Skills Essential: C1 Excellent strategic leadership
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computing (Rust, C, C++). Algorithm development for radio astronomy heterogeneous high-performance computing (CUDA, OpenCL) Basic Qualifications Bachelor's degree in EE or Physics. 5 years experience in
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the integration of both Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs). This position will involve training and optimizing these neural networks using Python frameworks, including CUDA
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Computing (HPC) knowledge around cluster builds, software, parallel computing, workload management, and cluster management. Knowledge with CUDA Programming Workflows, GPU programming, and GPU support
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using GPU/CUDA clusters for AI/ML and/or image processing. Proven ability to work in a dynamic environment and support large data systems. Effective documentation skills, including ability to prepare