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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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Division (MSTD) at Oak Ridge National Laboratory (ORNL), and who will focus on ORNL's continued development of methods to quantify shear (yield) strength of monolithic ceramic materials as a function of
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Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
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physics-informed and physics-ML hybrid approaches that integrate domain knowledge with data-driven methods to advance hydrological process understanding and prediction. Conduct multimodal, multiscale data
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calibration, coordinate-frame management, timestamp synchronization, and data acquisition across heterogeneous devices. Develop and evaluate multi-sensor localization and state-estimation methods that fuse
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still providing robust privacy guarantees. Develop novel privacy-preservation methods that accommodate the diverse privacy requirements of a large number of clients. Develop novel mathematically rigorous
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quantum magnetism and strongly correlated systems, as well as classical methods such as exact diagonalization, tensor networks or DMRG, and quantum Monte Carlo. Familiarity with inelastic neutron scattering
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research and development in the areas of gauge field generation, linear and eigen-solvers, or novel analysis methods. This position will reside in the Advanced Computing for Nuclear, Particle and
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the development and application of first principles methods and their incorporation into agentic AI workflows for materials discovery. Emphasis will be placed on robust, efficient, and sustainable software
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. In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of manufacturing processes Understanding