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Field
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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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metrics and usage statistics, identify inefficiencies on different levels (CPU/GPU, I/O patterns, etc.) and provide corresponding reports. You will work closely with researchers and HPC users and provide
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 6 days ago
, synchronization, scalability, and familiarity with GPU programming; strong familiarity with Unix/Linux tools; solid experience with version control, debuggers, compilers, and profiling tools (e.g., perf, gprof
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molecular dynamics simulations and was specially designed for parallelisation on GPUs. It is open source and licensed under the LGPL. Details can be found on the website https://halmd.org Job-Description
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or more GPUs; ability to work with pre-existing codebases and get a training run going Research interest in one or more of the following: Applied ML, Natural Language Processing, Computer Vision
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computing resources, with additional support involving HPC systems such as configuring GPU nodes, managing Slurm queues, containerising teaching notebooks, and enabling advanced pipelines Promote Robust
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research. Strong technical expertise in large scale data analysis, statistical and simulation tools, machine learning, AI and GPU programming. Experience with one more of these scientific collaborations
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configuration and management Knowledge of Linux and GPU scripting, storage management, quantum computing, and cloud systems Here's how to apply: Please submit your updated resume and a short cover letter
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
management, and security * Proven experience designing or supporting environments for AI/ML workloads (e.g., GPU nodes, distributed training, scalable storage) Preferred Qualifications, Competencies, and
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computing frameworks (e.g., MPI, NCCL) and model parallelism techniques. Proficiency in C++/CUDA programming for GPU acceleration. Experience in optimizing deep learning models for inference (e.g., using