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parallel/multiplexed assays, etc.) is desirable. Ability to interpret and discuss experiments and critically contribute to writing of manuscripts and grant proposals is expected. Well-organized, able
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to cutting-edge research aimed at transforming scientific data management and workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation of results. Deliver ORNL’s mission by aligning
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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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-driven techniques for the generation and exploration of complex, large-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel
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, Mixture-of-Experts; distributed training/inference (e.g. FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation pipelines for reasoning and agents. Federated & Collaborative
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Science, Computer Science, Applied Mathematics and Statistics, Electrical and Computer Engineering, Biomedical Engineering, or a related field. Experience with a deep learning framework like PyTorch. Strong
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-driven experiment design. Optimize pipelines for performance, parallelization, and near real-time operation during beam time. Contribute to simulation tools to test imaging concepts, predict performance
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design. Optimize pipelines for performance, parallelization, and near real-time operation during beam time. Contribute to simulation tools to test imaging concepts, predict performance, and support