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out experiments exploring the dynamics of high-intensity beams in the SNS ring. This project will explore a unique space charge mitigation technique based on coupled optics and phase space painting
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CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure platform management/partitioning
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include: (1) understanding dynamics in quantum materials; (2) investigating thermal transport, phase stability, and thermomechanical response through combined neutron-based measurements and complementary
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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competing structural phases and the vibrational and electronic structure in materials with defects and disorder. This effort will further seek to implement strategies to leverage machine learning techniques
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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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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evaluate applications based on candidate excellence and relevance of the proposed research topic. ● Proposed Seaborg Researcher research project ○ Creativity and innovation ○ Impact on program’s mission
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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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of existing frameworks and the development of new physics-based dynamic models. The candidate will lead the planning, coordination, and execution of experimental validation campaigns, ensuring strong