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Laboratory (ORNL). As part of our research team, you will closely collaborate with a team that includes condensed matter theorists, experts in neutron/X-ray scattering, and experts in thin film and single
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Postdoctoral Research Associate - Theory-in-the-loop of Autonomous Experiments for Materials-by-Desi
carlo), as well as experience in developing and/or applying advanced AI/ML methods to accelerate materials discovery. The project will involve integrating such theory-informed AI-models for creating
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
length/time scales, to provide improved mechanistic insights into nanomaterials response. Bulk of the work will be on novel materials for next-generation microelectronic devices (e.g. oxide ferroelectrics
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Requisition Id 15796 Overview: We are seeking a Manager who will focus on the management and oversight of the Accounts Receivable and Strategic Partnership Projects Funding Office (SPPFO) within
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Requisition Id 15696 Annual Salary Range: $125,000.00 - $156,000.00 Work-Site Type: Remote ORNL offers a flexible work environment that supports both the organization and the employee. In addition
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Requisition Id 15707 Overview: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges
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resides in the RF & Intelligent Systems Group in the Electrification and Energy Infrastructures Division, Energy Science and Technology Directorate at Oak Ridge National Laboratory (ORNL). As part of our
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specialty plus track record managing technical projects. Knowledge of a wide range of principles, theories, professional competencies & application techniques plus knowledge of other disciplines and functions
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), at Oak Ridge National Laboratory (ORNL). As part of our team, you will ensure that the instrumentation and controls-based equipment associated with fiber and network systems utilized throughout the Oak
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on defined domains; Fast and scalable algorithms to fit the proposed models to data, with a theory that explains the convergence and success of these techniques; Detailed re-analysis of the performance