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in 4D radar, thermal camera, SLAM or robotics software. Strong C++ and CUDA programming, with ROS1/2 experience. Experience in deploying SLAM on Jetson, ARM, or other edge platforms. Familiar with SLAM
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, successful experience with parallel programming using languages such as OpenCL and/or CUDA 7. Demonstrated, successful experience with source code version control systems such as Git, Subversion, or similar 8
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) environments 6. Demonstrated, successful experience with parallel programming using languages such as OpenCL and/or CUDA 7. Demonstrated, successful experience with source code version control systems such as
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and domains. Ability to troubleshoot connectivity issues and familiarity with vulnerability management and patching processes. Python and nVidia CUDA modules setup and configuration. Relational database
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techniques. Preferred Qualifications: Knowledge of HPC matrix, tensor and graph algorithms. Knowledge of GPU CUDA and HIP programming Knowledge on distributed algorithms using MPI and other frameworks such as
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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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++/Python/CUDA programming for real-time image processing. Experience in MRI pulse sequence programming, ideally on Siemens MRI platforms. Experience in MRI image reconstruction; motion and distortion
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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