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that have applications in multiple sectors of importance in the Department of Energy. The primary focus will be developing separation methods using mass spectrometry including microfluidics, MEMS, and mass
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quantum simulations of materials or chemical physics methods, in particular ab initio and AI-driven simulation methods Experience in statistical mechanical theory to analyze the simulation results Basic
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performance and detect anomalies Develop AI-driven predictive maintenance strategies to anticipate system failures or performance degradation Use AI/ML methods to optimize thermal system design parameters and
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. The position offers the opportunity to work at the interface of model development, observational data synthesis, and emerging AI/ML methods, in close collaboration with researchers from the SPRUCE (Spruce and
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. The position offers the opportunity to work at the interface of model development, observational data synthesis, and emerging AI/ML methods, in close collaboration with researchers from the SPRUCE (Spruce and
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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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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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, field observations and experiments, and advanced analytical techniques. Major Duties/Responsibilities: Develop and apply AI/ML methods to integrate heterogeneous geospatial, remote-sensing, hydrological
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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