30 structures-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" positions at Brookhaven National Laboratory in United States
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reactivity under realistic conditions. A central aspect of the role is the derivation of interpretable descriptors from electronic structure calculations and the application of machine-learning methods (e.g
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structural biology. We also leverage the unique capabilities of Brookhaven's National Synchrotron Light Source-II and Center for Functional Nanomaterials; major research facilities open to scientists from
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Spectroscopy (XAS) data. Position Description Working at the Inner Shell Spectroscopy (ISS) and Quick Absorption and Scattering (QAS) beamline, which provides intense hard X-rays for probing structure and
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Order 486.1A. You will be asked to disclose any such participation at the time of hire for review by Brookhaven. The full text of the Order may be found at: https://www.directives.doe.gov/directives
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Order 486.1A. You will be asked to disclose any such participation at the time of hire for review by Brookhaven. The full text of the Order may be found at: https://www.directives.doe.gov/directives
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of the Order may be found at: https://www.directives.doe.gov/directives-documents/400-series/0486.1-BOrder-a/@@images/file We are not accepting applications for this job through AcademicJobsOnline.Org right now
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for review by Brookhaven. The full text of the Order may be found at: https://www.directives.doe.gov/directives-documents/400-series/0486.1-BOrder-a/@@images/file
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). The successful candidate will conduct research at the forefront of electron microscopy to elucidate structure-property relationships in quantum materials and devices, including material origins of quantum
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for review by Brookhaven. The full text of the Order may be found at: https://www.directives.doe.gov/directives-documents/400-series/0486.1-BOrder-a/@@images/file
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automates building and modifying surface structures, submitting DFT calculations, post-processing electronic structure and vacancy energies, and extracting machine-learning descriptors for modeling oxygen