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Geosciences division is hiring an Autonomous Material Processing Postdoctoral Fellow to help transform how critical materials are produced. In this role, you'll bring together machine learning, real-time
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vision for extracting complex patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms leading to new applications of machine learning and artificial intelligence
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for binding critical minerals and materials. The postdoc will be part of a collaborative team of researchers in the Molecular Foundry from the Biological Nanostructures, Data Science and Infrastructure, Imaging
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spins to correlated spin arrays and prototype device architectures. This position contributes to the development of molecular quantum materials within a DOE user facility environment, leveraging
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positively to the active research culture. Uphold a culture of safety and promote stewardship values of the laboratory. We are looking for: PhD in Physics, Materials Science, Chemistry or equivalent. Ability
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reasoning over large scientific data sets. Developing theories for materials synthesis and degradation. Preparing presentations and reports. Writing peer-reviewed journal articles. We are looking for: PhD in
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tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or a related numerical field. Programming experience
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-tuning and/or Retrieval-Augmented Generation (RAG) methods to augment LLMs with dedicated knowledge in transportation and electric grid domains. This involves designing methods to process input data and
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of Advanced Quantum Testbed (AQT) and Quantum System Accelerator (QSA). This position will focus on design, calibration, and operation of mid-scale digital superconducting quantum processors. These processors