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for applications. The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems
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research program at the interface of theory and experiment. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physical chemistry, inorganic chemistry
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theories. The candidate will also be expected to apply these models to predict the engineering behavior of materials in service environments. Position Requirements Recent or soon-to-be-completed PhD
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control and measurement, superconducting circuits, nanofabrication, and low-temperature experiments is strongly preferred. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years
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Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in chemistry, materials science, chemical engineering, physics, or a related field. Solid foundation and essential
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-be-completed PhD (typically completed within the last 0-5 years) in chemistry, chemical engineering, materials science or related field. Knowledge and hands-on experience in the following areas
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), in collaboration with Argonne researchers and external partners from industry and academia. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in
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requires hands-on laboratory work to develop and synthesize and analyze new organic materials, specifically new battery electrolyte materials. Position Requirements Recent or soon-to-be-completed PhD
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help shape technologies with broad relevance to future quantum systems. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physics, electrical engineering
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. Position Requirements A formal education in materials science and engineering, nuclear engineering, or a related field at the PhD level with zero to five years of employment experience. Knowledge of metallic