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Field
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VR development for educational uses Good hands-on experience in programming, e.g., C/C++/C#, CUDA, Python, and scripting Track record in research and publication particularly in education Strong
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efficiency for serving massive models. Research and implement cutting-edge optimization strategies at the kernel level (e.g., FlashAttention, custom CUDA/ROCm kernels). Build robust data pipelines
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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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Experience with Pytorch, MONAI, CUDA or equivalent software libraries for developing deep learning models. Familiarity with medical image such as MRI, CT, or volumetric ultrasound. Knowledge on common medical
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skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models Experience with Pytorch, MONAI, CUDA or equivalent software
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research related software, Python, R, Matlab, Mathworks, Julia, Ansys, Intel, nVidia cuda and GCC compilers. Experience with dev ops tools such as GitHub, GitLab, Ansible, package management tools for rpm
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(e.g., MPI, OpenMP, CUDA) and high-performance interconnects (e.g., InfiniBand). Preferred Qualifications: Familiarity with advanced storage solutions and parallel file systems (e.g., Lustre, GPFS
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techniques and probability theory ● GPU Programming: Experience with GPU programming and optimization for ML models, utilizing frameworks, like CUDA or OpenCL ● Experience with applied computer vision, such as
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WEKA, VAST, GPFS, BGFS, CEPH. Experience with installing and supporting: Open source and commercial research related software, Python, R, Matlab, Mathworks, Julia, Ansys, Intel, nVidia CUDA and GCC
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languages used: C++ 2014, CUDA, Lua Desirable: - interest in high-performance computing with graphics processors (GPUs) and simulation methods - fluent knowledge of modern C++ and a scripting language