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The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge computational and systems biology research. The primary focus
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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for candidates interested in the intersection of complex oxide epitaxy, quantum information, and nanophotonic to contribute to high-impact science at a national user facility. Key Responsibilities Develop and
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math, HPC, signal processing, computational physics and materials science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of
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electrocatalyst materials Plan and execute in situ/operando studies using advanced techniques such as X-ray Absorption Spectroscopy (XAS), X-ray Photoelectron Spectroscopy (XPS), Raman spectroscopy, Differential
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experience in economic and supply chain analysis, computational modeling, or policy analysis. Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, NumPy
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chemistry, chemical engineering, physics, computational science, materials science, or related field. Background in synchrotron characterization techniques. Experience collecting and analyzing large
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of advanced scanning/transmission electron microscopy (S/TEM) methods for cutting-edge scientific research in areas such as quantum materials and low-dimensional energy systems. This position emphasizes
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and presentation Position Requirements Recent or soon-to-be-completed Ph.D. (0–5 years) in materials science, physics, electrical engineering, or a closely related field Strong background in scanning
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strong track record in problem solving and scientific publications. The candidate will be expected to conceive of, plan, and implement the scientific research, and to report relevant results in