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for deployment on large-scale computing resources, such as high-performance computers (e.g., Perlmutter, Aurora, etc.). This includes tasks such as automating model design, optimizing hyperparameters, and training
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: Optimize Retrieval-Augmented Generation (RAG) techniques to improve the relevance and contextual accuracy of LLM-generated content. Explore and apply multimodal LLMs capable of effectively processing and
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such as PyTorch and TensorFlow. Experience with high-performance computing and/or scientific workflow. Strong background in inverse problems, numerical optimization and image processing. Job Family
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field - expertise in applying scientific knowledge and conducting practical laboratory research Extensive experience in synthesizing and optimizing host materials for lithium-sulfur batteries Hands
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modeling tools to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
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that integrate simulation, machine learning, and data analysis. Numerical optimization methods (e.g. machine learning including deep neural networks, reinforcement learning, data mining, genetic algorithms
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resulting from the deployment of new technologies. If you are passionate about sustainable energy solutions and optimizing supply chains for a more energy-secure future, we invite you to apply
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, including object stores, memory-X and filesystems, to research and develop strategies to evaluate, profile and optimize AI applications at scale. Another key objective is to help us with design of future