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for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of new computational techniques specifically
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of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as
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architectural models, system-level simulators, and performance modeling frameworks for QHPC systems, capturing relevant characteristics of quantum processing units, classical HPC resources, interconnects, system
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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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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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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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-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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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation