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Field
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researchers to integrate computing techniques into research activities using common HPC programming languages, tools, and techniques including Fortran and/or C/C++, MPI, OpenMP, CUDA An equivalent combination
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Programming: Python, CUDA, Git
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models 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
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rendering into medical imaging workflows. A major focus will be on accelerating inference and training using GPU-optimised components, including custom CUDA kernels. This role offers a unique opportunity to
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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 knowledge and hands-on experience
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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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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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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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performance computing using SLURM or LSF Experience with PyTorch, JAX, or Tensorflow Experience with NVIDIA CUDA and related OpenMP programming Experience with cloud services (AWS, GCP, Azure, etc) Experience