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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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Argonne’s Nanoscience and Technology Division seeks a postdoctoral scientist to advance transmission electron microscopy (TEM) studies of materials and interfaces relevant to microelectronics
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techniques, and carry out any required data analysis. Position Requirements • Experience working with Lorentz Transmission Electron Microscopy. • Strong background in Materials Science or Physics. • Min
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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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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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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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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