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skills. Other desirable criteria include: Image analysis experience, e.g. automated segmentation and/or image registration. Experience using high performance computing clusters/GPU based parallelisation
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and/or image registration. Experience using high performance computing clusters/GPU based parallelisation. Experience solving diffusion or related equations by Monte Carlo methods. Knowledge of MRI
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GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices
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Shifting bits: Adaptive numerical precision for GPU software in particle physics and beyond (S3.5-COM-Richmond) School of Computer Science PhD Research Project Competition Funded Students Worldwide
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administrative use of Macs and Windows, as well. The Division's research and teaching bring a strong requirement for GPU compute and mass data storage, with GPU workstations across large parts of the Division, HPC
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assigned by supervisor. Requirements Bachelor’s degree or higher in Robotics, Computer Science, Mechanical Engineering, Control Engineering, or a related discipline. Hands-on experience with at least two
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engineering applications through the exact same governing equations. The software for this work is our state of the art open source multiphysics weakly compressible SPH solver DualSPHysics [3] with GPU hardware
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computational experience, preferably on HPC systems with knowledge of parallelisation techniques and GPU programming. You will be expected to plan your own research, with guidance if required, and to assist in
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AI pipelines (similar to the attached prototype). Enable users to configure model parameters, connect modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU
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, scientific computing, etc). Strong scientific computing background, with experience of different architectures (e.g. CPUs/GPUs) and their use in high-performance computing through shared or distributed