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improve access to divertor-relevant target exposure conditions. Validate simulation results against experimental data and constrain code inputs to reduce uncertainty in predictions for future experiments
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-assisted model calibration, or pattern detection in large observational datasets) to improve nutrient-cycling representation and reduce predictive uncertainty in models. Collaborate with an interdisciplinary
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-assisted model calibration, or pattern detection in large observational datasets) to improve nutrient-cycling representation and reduce predictive uncertainty in models. Collaborate with an interdisciplinary
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Postdoctoral Researcher in the fields of uranium chemistry and nuclear forensics. The role entails performing and leading cutting-edge research on nuclear fuel cycle-related materials with a focus on solid
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computing, with a particular emphasis on methods tailored to study nonequilibrium quantum many-body systems. The position offers an exciting opportunity to contribute to cutting-edge research
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AI-ready scientific data. As a postdoctoral fellow at ORNL, you will collaborate with a dynamic team of scientists and engineers, leveraging cutting-edge resources; most notably the Frontier
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hybrid quantum-classical computing approaches and investigate architectural, performance, resource, and scalability tradeoffs. The position provides opportunities to conduct cutting-edge research
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innovation and play a key role in driving forward significant advances in fiber technologies. Major Duties/Responsibilities: Developing cutting-edge polymer chemistries for advanced fiber applications
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team that includes condensed matter theorists, experts in neutron/X-ray scattering, and experts in thin film and single crystal materials growth and characterization. You will perform cutting-edge
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-preservation algorithms. The successful candidate will develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work