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automation, high-throughput data acquisition, and real-time data processing, offering a unique opportunity to advance S/TEM capabilities, publish impactful research, and collaborate with a diverse group of
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candidate will lead efforts in materials synthesis, in situ/operando characterization, and catalytic performance evaluation. This role offers a unique opportunity to leverage CNM’s advanced characterization
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facilities and instrumentation, publish in high-impact venues, and collaborate closely with scientists across CNM, Argonne, and external partners. Key Responsibilities Design, synthesize, and rigorously
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candidate will conduct research related to a wide range of topic areas including the analysis of energy and power systems, optimization of veriable energy resources, electricity market design and operation
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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samples with significantly higher temporal resolution than traditional scanning methods. The selected candidate will simulate and design the experimental setup, and then perform single-frame ptychography
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morphology, product quality, and process efficiency at engineering scales. With guidance, the appointee will : Perform experiments with multimodal sensors to advance the technical understanding and application
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The Cosmological Physics and Advanced Computing (CPAC) group at Argonne National Laboratory invites applications for a postdoctoral researcher to work closely with Dr. Lindsey Bleem
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in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
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mechanisms of photoinduced charge transfer and spin state conversion across a wide spectrum of systems, including small transition metal complexes and donor-acceptor systems. By performing state-of-the-art