78 phd-engineer-machine-learning Postdoctoral positions at Oak Ridge National Laboratory
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respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in mechanical engineering, electrical engineering, or a related discipline obtained in
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challenges facing the nation. We are seeking a Postdoctoral Research Associate who will support the Quantum Sensing and Computing Group in the Computational Science and Engineering Division (CSED), Computing
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Science and Technology Directorate at Oak Ridge National Laboratory (ORNL). Our research team carries out high-quality R&D focused on the development of new, high performance, lower-cost, domestically
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. Basic Qualifications: A PhD in materials science and engineering or a related discipline completed within the last five years. A strong background in physical metallurgy Preferred Qualifications
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opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in materials science and engineering or a related discipline
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Requisition Id 17078 Overview: We are seeking a Postdoctoral Research Associate to join the Data Science and Engineering for Nonproliferation Group in the National Security Sciences Directorate
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compilers and runtimes can unify classical, quantum, and analog execution models under a shared optimization framework. Basic Qualifications: Ph.D. in Computer Science, Computer Engineering, or a closely
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in the Sustainable Manufacturing Technologies Group in the Manufacturing Science Division (MSD) of the Energy Science and Technology Directorate at Oak Ridge National Laboratory (ORNL). Our research
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte