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microscopy of materials and nanostructures for electronics. This capability at Argonne’s Center for Nanoscale Materials enables imaging of electrically driven dynamics with simultaneous nanometer-scale spatial
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leverage state-of-the-art in-situ transmission electron microscopy (TEM), including Lorentz TEM, and will have the opportunity to utilize other advanced techniques, such as ultrafast electron microscopy
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
both within and outside the laboratory. Preferred Knowledge, Skills, and Experience Hands-on experience with liquid transmission electron microscopy (TEM). Knowledge of electrocatalysis, especially CO₂RR
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ellipsometry, atomic force microscopy, and scanning electron microscopy is required. Knowledge of atomic layer deposition and materials for energy storage applications is highly desirable. The successful
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facilities, who are developing complementary microscopy and AI/ML workflows, ensuring that multimodal datasets (X-ray, electron microscopy, and spectroscopy) are well-aligned and interoperable. These positions
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time-resolved hard X-ray diffraction microscopy and spectroscopy on single-crystalline bulk and thin film quantum materials (e.g. ferroelectrics, multiferroics, strongly correlated electron systems
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the electronic, magnetic, and optical properties of 2D materials at ultrafast timescales, which holds promises for developing new energy technologies. The candidate is responsible for conceiving, planning, and
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Plan and execute in situ/operando experiments using advanced characterization methods, including Near Ambient Pressure X-ray Photoelectron Spectroscopy (NAP-XPS), electron microscopy, Raman spectroscopy
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thin-film growth and characterization (oxides preferred), using sputtering (preferred), PLD, or MBE. Familiarity with synchrotron-based X-ray techniques and electron microscopy Knowledge of quantum
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relationships in next-generation electronic materials. This role involves creating AI models for real-time data analysis, enabling autonomous experiments through active learning and "curiosity-driven" exploration