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Field
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. • Experience with image processing. • Experience with Intel IPP, Intel MKL, Intel oneAPI, Matlab, CUDA, and real-time audio processing. • Experience with C++, Java, and Python. *Incumbent will be
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). You have good experience in Python/Matlab and can learn a new programming language rapidly. Experience with other image processing tools & libraries (e.g. OpenCV, PyQt, CUDA) is a plus. You are used
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, CUDA) and good understanding of hardware used in large scale HPC clusters such as hybrid CPU+GPU systems, memory hierarchies and file systems; experience with job schedulers (e.g., Slurm, FLUX) and
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current practice). Demonstrated expertise in AI/ML. Proven track record in application performance optimization. Advanced experience with parallel programming models (e.g., OpenMP, MPI, CUDA). Extensive
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with job schedulers, e.g. SLURM, PBS, SGE, etc. ● Experience working at an academic institution ● Experience with parallel codes and libraries (e.g. MPI, OpenMP, Cuda) ● Experience with research and/or
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dynamics (algorithms and current practice). Experience in AI/ML. Experience in application performance optimization. Familiarity with parallel programming models (e.g., OpenMP, MPI, CUDA). Experience
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current practice). Demonstrated expertise in AI/ML. Proven track record in application performance optimization. Advanced experience with parallel programming models (e.g., OpenMP, MPI, CUDA). Extensive
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using the shared memory and message passing techniques. Knowledge of OpenMP and MPI or similar programming directives and libraries. Knowledge of GPU programming with CUDA, HIP, oneAPI or OpenMP for GPUs
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using the shared memory and message passing techniques. Knowledge of OpenMP and MPI or similar programming directives and libraries. Knowledge of GPU programming with CUDA, HIP, oneAPI or OpenMP for GPUs
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and train CNN and SNN models utilizing frameworks such as Keras, PyTorch, and SNNtorch Implement GPU acceleration through CUDA to enable efficient neural network training Apply hardware-aware design