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-based CPUs and accelerators such as NVIDIA or AMD GPUs. ● Experience working in an academic research computing center or large-scale HPC environment. ● Experience with GPU computing and
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for Computer Graphics and Real-Time Rendering. By using ANNs, coded for high-performance on cross-vendor GPUs, we aim to create new techniques for global illumination and material models. The subject works with
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infrastructure, model training, and inference systems. You'll design, develop, and optimize scalable data pipelines and build multi-node GPU training and inference pipelines for foundational models. You'll also
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frameworks). Experience using open-source model ecosystems such as Hugging Face (Transformers, Datasets, Accelerate). Experience using or supporting supercomputing or GPU-enabled clusters. Experience with data
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. Duties: Develop and execute a strategic roadmap for research computing infrastructure, including GPU-enabled high-performance computing (HPC) environments and enterprise storage systems including
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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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research, research groups require access to high performance data storage for data-intensive processing, visualization for data analysis, GPUs, access to cloud services and research infrastructure skills
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Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or JAX Experience with HPC and GPU-accelerated computing Familiarity with foundation models / LLMs; interest in reproducible
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of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a significant computational component in deploying multi-GPU codes
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agentic workflow (Llama, Claude, LangGraph, etc) Experience with training models on GPUs Experience working with Unix and/or Linux, including shell scripting Experience implementing NLP/AI algorithms and