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Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted visual representation and analysis of large-scale 2D/3D scientific data
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reactions, as well as nuclear data. The position is part of the nuclear physics team that resides in the Advanced Computing for Nuclear, Particle, and Astrophysics group at the National Center
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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/scalable
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large-scale training and post-training pipelines (including distributed data/compute and evaluation harnesses). Collaborate with domain scientists and external partners; co-develop end-to-end AI workflows
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safety at ORNL and DOE sites. This position resides in the Performance Engineering group in the Data and AI Systems Section in Computer Science and Mathematics division within Computing and Computational
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solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and AI processes (e.g., model training, inference). Develop agentic AI systems and AI harnessing
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techniques capable of maintaining relationships between data and metadata. Collaborate on innovative solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and
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Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement
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, including applied mathematics and computer science, experimental computing systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis
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). Knowledge of high-performance computing or cloud environments for large-scale data. Strong collaboration skills and ability to work in interdisciplinary teams. Special Requirements: Applicants cannot have