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faculty onboarding, consultation, documentation, and training activities that help research teams become productive users of HPC and GPU resources Assist faculty in identifying and pursuing external
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· Experience with multi-GPU or distributed training is a plus · Ability to work independently as well as part of an interdisciplinary team in a fast-paced environment, while making necessary connections
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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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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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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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population and comparative genomics to examine genetic diversity, selection, pangenome relationships, and functional conservation. You will also develop reproducible GPU- and CPU-based high-performance
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programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image
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. Demonstrated experience with training and calibrating other large-scale complex models. Demonstrated ability to pretrain large-scale models from scratch, including distributed multi-GPU training. Demonstrated
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, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This position is a prestigious Empire AI Fellowship
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Processing Unit (GPU) hardware. Working Conditions Needs to be able to successfully perform all required duties. Office/research environment; some travel and weekend work is required. UTRGV is a distributed