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Field
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expertise in optimisation; Computer Science, with expertise in design and analysis of algorithms and high-performance (GPU) computing; Industrial and Systems Engineering, with AI in process mapping and Conops
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programming skills in Python, MATLAB, C++, or similar scientific computing environments, including high-performance and GPU computing. A strong scientific track record, evidenced by high-quality publications
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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scanning, virtual reality and on-demand wide-area surveillance video feeds. In addition Durham University hosts the UK regional supercomputer Bede (128 NVIDIA V100 + 3 NVIDIA Hopper GPUs) in
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available to a University in the world. It includes 49,920 AMD cores for traditional computing and 608 NVIDIA GPUs for artificial intelligence analyses. The University of Florida Genetics Institute is an
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preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against existing methods. Integrate and analyze large
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GPU-centric communication techniques. Collaboration & impact: We actively encourage you to publish your research in high-profile international venues, providing the opportunity to contribute directly to
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trading strategies. We work with petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. Depending on the day, we might
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transfer (CHT) or fluidic manifold optimization Experience with machine learning/AI (PyTorch or TensorFlow), reduced-order modeling (ROM), or data assimilation (DA) Experience with GPU programming (CUDA and
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system diagnostics. Experience with CUDA and deployment on NVIDIA edge-GPU platforms (Jetson Orin, Thor, or Spark). Experience calibrating and synchronizing LiDAR, IMU, camera, and wheel-odometry systems