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chemistry and experience with quantum chemistry packages (e.g., Molpro, NWChem) Strong skills in developing and implementing computational and numerical methods; familiarity with parallel computing on CPU/GPU
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, and interacting with pilots and passengers. Operates and becomes familiar with ground support equipment, such as the aircraft tug, ground power unit (GPU), lavatory service cart, de-icing cart, forklift
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computing (HPC) systems, including GPUs, and programming, such as using CUDA, MPI, AI/ML/DL, and advanced debuggers and performance analyzers. Familiarity with working on open-source projects. About UF
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optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a
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-on experience developing generative models Is highly proficient in PyTorch and/or JAX Has experience training large-scale neural networks on HPC or GPU clusters Has experience with representation learning and
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required to maintain a new GPU cluster at KMI, spending no more than 20% of the FTE. The anticipated starting date is between April 2026 to October 2026. The appointment will be initially 2 years and may be
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(static and dynamic photochemistry, heterogeneous catalysis, modeling of interfaces and ionic liquids). It benefits from access to the CBPSMN mesocenter, with a large amount CPUs and GPUs facilities. In
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://shimadzuinstitute.org/ Faculty members have excellent access to computational resources, including Texas Advanced Computing Center (TACC), and multiple HPCs on campus, including some GPU-heavy clusters as
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). Experience managing systems utilizing GPU (NVIDIA and AMD) clusters for AI/ML and/or image processing. Knowledge of networking fundamentals including TCP/IP, traffic analysis, common protocols, and network
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architectures. This includes among other: (a) design and implementation of machine learning and GenAI models, (b) efficient training and inference on GPU-based systems, (c) fine-tuning and optimization of large