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of environmental factors of 60,000 subjects across multiple time points. Our research laboratory has great computing capacity, including multiple H100 and A100 GPU systems for deep learning, and computing clusters
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scientific domains. Preferred Experience: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling
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for recruitment positions and for general criteria for the position. Preferred selection criteria Knowledge of in Norwegian/Scandinavian language Experience with GPU based systems Experience with HPC based
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at the realization of large-scale diffusion language models and flow models. Specifically, tasks include but are not limited to: training using parallel GPUs, improving diffusion language models and flow models
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. Applicants are expected to have strong programming skills in Python, hands-on experience with PyTorch, and practical experience with GPU computing. Experience with engineering simulation, computational
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. Experience with heterogeneous and parallel computing technologies, programming models, or accelerators, including CPUs, GPUs, FPGAs, and emerging computing technologies. Familiarity with quantum software
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pretraining, representation learning, or model evaluation. Experience with PyTorch and the Hugging Face ecosystem. Experience with high-performance computing, SLURM, distributed multi-GPU training, or large
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 3 months ago
development, including grant writing and academic presentation. Have access to the largest academic computing cluster in the world, including the largest GPU cluster. Application Materials Applicants should
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institutional clusters. Write robust Linux bash scripts and job submission scripts for SLURM and PBS environments, including multi-node GPU/CPU workflows, monitoring, restart, and post-processing pipelines
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CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure platform management/partitioning