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for next-generation lithium- and sodium- ion batteries. This role provides a unique opportunity to work at the forefront of battery innovation, focusing on the design and implementation of novel synthesis
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probes (e.g., synchrotron based X-ray reflectivity, optical holographic interferometry, scanning probe microscopy). Work includes the experimental design, interpretation of data, and the presentation
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organometallic / inorganic heterogeneous catalysis Design, synthesize, and characterize metal-ligand complexes supported on metal oxide and/or non-traditional support materials Investigate the catalytic activity
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at Materials Engineering Research Facility (MERF) and collaborators inside and outside Argonne. The candidate is expected to design and conduct experiments, analyze data and explore mechanisms behind
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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
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. The candidate will be expected to conceive of, plan, and implement the scientific research, and to report relevant results in publications and conference presentations. The selected individual will have access
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researchers in the Electrochemical Energy Storage Department and partners, as well as collaborators at both on-site and off-site facilities, to design and perform experiments that advance understanding in
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modeling is critical Considerable computational expertise in using quantum mechanical methods to calculate reaction mechanisms and kinetics in heterogeneous systems is essential Ability to program in C++ and
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
vulnerabilities. The Postdoctoral Appointee will be responsible for the conceptual framework, design, and implementation of these models, ensuring scalability on the DOE’s leadership computing facilities. Position
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physics knowledge into DL model design and training, these models outperform traditional methods even without labeled training data (https://www.nature.com/articles/s41524-022-00803-w ). Application spaces