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Requisition Id 16492 PDAA Overview: We are seeking a Postdoctoral Research Associate with a strong background in organic synthesis to develop, characterize, and test new molecules for the chelation and separation of metal ions relevant to isotope production and energy. This position...
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Requisition Id 17231 Overview: We are seeking a Postdoctoral Research Associate who will focus on the development, application, and optimization of an active learning workflow for dataset
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: Research and development of scalable AI, ML, and deep learning models using the Oak Ridge Leadership Computing Facility (OLCF) systems. Conduct research with scalable transformer-based foundation models with
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machine learning techniques to improve DER/IBR model calibration, parameter estimation, uncertainty assessment, and decision support. Build and apply hardware-in-the-loop test platforms to validate the full
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typical for an individual to be offered a salary at or near the top of the range for a position. Link to benefits. https://jobs.ornl.gov/content/Benefits/?locale=en_US Overview: We are seeking
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learning techniques. Preferred Qualifications: Experience with microstructure characterization techniques (SEM, EBSD, TEM, XRD). Experience in mechanical testing (tension, compression, fatigue, creep
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, including correlation analysis and machine learning techniques. Preferred Qualifications: Experience with microstructure characterization techniques (SEM, EBSD, TEM, XRD). Experience in mechanical
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Requisition Id 16869 Overview: We are seeking a Postdoctoral Research Associate to use advanced scanning probe microscopy for the development and application of multiscale excitonic probes. In this role, you will study buried interfaces, defects, and charge-transfer processes in vertically...
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AI for science, including scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models on leadership-class supercomputers. You’ll help design
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. Develop novel mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning