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understanding of the physics of the QAHE necessary to design and develop new quantum resistance standards. Additional applications in quantum information science (QIS) can be envisioned for robust QAHE devices
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RAP opportunity at National Institute of Standards and Technology NIST Metabolomic and Lipidomic Research: Emphasizing Advanced Data Analysis, Metabolite/Lipid Annotation, and Functional Pathway Elucidation Location Material Measurement Laboratory, Chemical Sciences Division opportunity...
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also difficult; currently available wavefunction diagnostics are not yet reliable for this purpose and partition functions include uncontrolled approximations. key words Ab initio; Computational
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these models do not account for realistic conditions and require lengthy computational time. In order to overcome the practical challenges and numerical bottlenecks, the Fire Research Division of NIST’s
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requires expertise in Computer Science, Statistics, or a similar field. Experience with machine learning, genetics, and/or bio-informatics is strongly preferred. The postdoc will work together and within a
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We develop and utilize state-of-the-art experimental and computational techniques to acquire, evaluate, and correlate thermodynamic data of standard reference quality with a particular emphasis on
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RAP opportunity at National Institute of Standards and Technology NIST Investigating Time-dependent or Transient Structures of Complex Soft Matter Materials Driven by External Stimuli Using Neutron Scattering Location NIST Center for Neutron Research opportunity location 50.61.01.C0477...
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RAP opportunity at National Institute of Standards and Technology NIST Chip-scale atomic magnetometers Location Physical Measurement Laboratory, Time and Frequency Division opportunity location 50.68.82.C0807 Boulder, CO NIST only participates in the February and August...
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NIST only participates in the February and August reviews. This program involves multimodal imaging techniques that use magnetic resonance imaging (MRI) as either a base or as a complimentary
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catalytic turnover. Integrative modeling and machine learning have the promise of establishing new tools for combining computational and experimental data from HDX-MS and NMR to explain the dynamics and