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Design Group in the Materials Science and Technology Division (MSTD), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL). This position lives in the Alloy Behavior and Design Group
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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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Science and Technology Division (MSTD), Physical Sciences Directorate (PSD) at ORNL. Major Duties/Responsibilities: Develop and use first principles methods to describe electronic and magnetic structure
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solutions to compelling problems in energy and security. We are seeking an outstanding Postdoctoral Research Associate with a strong background in condensed-matter physics and materials science – especially
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and Polymer Chemistry Section, Chemical Sciences Division, Physical Sciences Directorate, at Oak Ridge National Laboratory (ORNL). This METALLIC (Minerals to Materials Supply Chain Facility) project
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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. Basic Qualifications: A PhD in Materials Science & Engineering, Physics, Chemistry, or a related field completed within the last 5 years A minimum of 2 years of post-Ph.D. experience utilizing
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and software tools for visualizing and analyzing materials characterization data Develop novel, data-driven materials characterization workflows Advance understanding of process-structure-property
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solutions to compelling problems in energy and security. We are seeking an outstanding Postdoctoral Research Associate with a strong background in condensed-matter physics and materials science and expertise
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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