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simulation codes require a mesh to discretize the spatial domain of interest. For some applications, generating this mesh has historically been a labor-intensive process because it is shown to be a leading
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simulation codes require a mesh to discretize the spatial domain of interest. For some applications, generating this mesh has historically been a labor-intensive process because it is shown to be a leading
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workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed
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computing for science and engineering; focuses on grand-challenge science and engineering applications; procures largest-scale computer systems (beyond typical vendor design points) and develops high-end
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with a particular focus on AI for Science, Natural Language Processing (NLP), and AI for Operations. Our aim is to push the boundaries of computational science by integrating advanced AI methods with
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clustered computing software solutions to researchers who process large data sets, run simulations, and develop scientific code as part of their projects. Design, develop, optimize, and maintain scientific
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for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation
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-1000 experiment. Develop new low-background materials, scintillators, and production techniques, including custom synthesis of PEN and clean additive manufacturing processes Integrate AI/ML
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architectural design decisions for massively parallel heterogeneous systems, including pathways that integrate quantum or analog co-processors. Performance Engineering at Scale: Profile, model, and optimize
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, enhanced sampling, QM/MM) Experience improving performance and scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation