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Requisition Id 15794 Overview: The Physics Division at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to join the Nuclear Structure and Nuclear Astrophysics
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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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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications A Ph.D. in nuclear or health sciences (such as health physics
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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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by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in physics or a related field completed within the last 5 years
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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
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reproducibility in multidisciplinary science. Application Process: Applicants should submit their CV, a research statement detailing their expertise and vision for intelligent workflows and AI-powered provenance
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. Research will involve growth of single crystals and measurements to understand their structural and physical properties including magnetism and thermal transport, as well as helping to identify new magnetic
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
. Focus will largely be in developing and deploying such AI/ML algorithms, closely collaborating with theorists and experimentalists to realize physics- models and/or physics-aware ML-models that can bridge