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techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows. • Investigate strategies to balance performance and resilience across heterogeneous computational
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Requisition Id 15447 Overview: The Data and AI Systems Research Section/Workflow systems Group within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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Computer Science, Electrical/Computer Engineering, AI/ML, or a closely related field. Demonstrated experience in AI/ML model development, LLM tuning, generative AI, functional safety and risk analysis. Proficiency
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breaking in nature, the limits of nuclear stability, and signatures of new physics beyond the Standard Model. Major Duties/Responsibilities: Develop formalism and methods for computing properties of nuclei
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, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self-motivated
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computing AI on High-Performance Computing (HPC) cluster. Examples on areas of research interest include but are not limited to: Vision transformers. AI foundation models. Computing and energy-efficient
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or machine learning potentials (iv) modeling of the solid and aqueous interfaces. Research proposal or concept writing experience. Programming experience for workflow development and scientific computing