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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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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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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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scripting methods (i.e. Python, MATLAB, C++, CUDA, Bash, and/or SQL) and machine learning / deep learning methods Active learning, exploration, optimal experiment design, Bayesian optimization, reinforcement
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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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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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, 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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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
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mathematics, e.g., probability theory, linear algebra, differential/integral calculus Prior programming experience in Python is a must, C++ and CUDA experience are a plus Hands-on experience in working with