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frameworks like PyTorch, Hugging Face, sklearn, tensorflow. Excellent verbal and written communication skills Experience with GPU training and handling large medical datasets e.g., large magnetic resonance
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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
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. CPUs/GPUs) and their use in high-performance computing through shared or distributed parallel programming (e.g. OpenMP, MPI). Strong programming ability in C++ or a related language. Experience in
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/machine learning software development, and even writing software for GPUs or supercomputers. The Person The successful candidate should be able to demonstrate: Currently in the second year of a relevant
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vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with
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Solving these decision problems often requires tackling the Bellman equation, which can be computationally prohibitive. This project will go beyond classical methods by developing -Parallelised, GPU-enabled
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scale (multi petabyte) data storage infrastructure which may include 3rd party filesystems, management tools and interfaces. • High performance computing clusters. Currently slurm-based with CPUs, GPUs