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will involve designing beam dynamics experiments, measurement, simulation, and data analysis. This position resides in the Accelerator Physics Group in the Accelerator Science and Technology Section
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systems (proteins, enzymes, membranes, and complexes) Integrate AI/ML approaches with physics-based simulations to accelerate discovery and improve predictive fidelity Contribute to cross-scale modeling
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Requisition Id 16254 Overview: Do you have a passion for applying AI methods for accelerating scientific discoveries and an ability to think outside of the box in a collaborative and open
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challenges and conduct the research needed to accelerate the delivery of solutions to the marketplace. The Radiation Transport and HPC Methods (RTHPCM) Group within the Nuclear Applications Methods and Data
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materials. In this role, you will develop and apply methods that integrate physics‑guided image correction with intelligent (AI/ML‑enabled) data‑acquisition strategies. Key objectives include (1) implementing
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to accelerate the design and discovery of novel materials. The Materials Theory Group has a background in using first principles methods to examine electronic and thermal transport, magnetic properties
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Section, Chemical Sciences Division, Physical Sciences Directorate, at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Conduct atomistic simulations to contribute to multi-disciplinary
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physics, fusion research, life sciences, and materials science. Furthermore, these efforts to enhance data readiness for AI workflows may play a significant role in contributing to the goals of the 2025
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physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team
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implementation on hybrid quantum-classical hardware, and collaboration with a multidisciplinary team of researchers in quantum computing and computational condensed matter physics. The ideal candidate will have a