18 phd-agent-based-modelling Postdoctoral positions at Brookhaven Lab in United States
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, and Abilities: Experience with neutron or x-ray scattering from single crystals Experience with characterizing magnetic and structural dynamics using neutron scattering Modeling neutron scattering from
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. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design is based on the existing and highly optimized
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Research program. The project aims to integrate a diverse suite of high-resolution observations (atmospheric, land surface, and infrastructure), diagnostic/predictive models, and civic engagement to provide
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based on two first-of-their-kind NION scanning transmission electron microscopes. In this postdoc position, you will be a member of the Electron Microscopy group and be mentored by scientists at CFN
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Abilities: Experience with topological quantum materials High pressure processing including spark plasma synthesis Experience with dilution refrigerators Synchrotron based materials characterizations
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on diverse scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and adaptation
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applies platforms for state-of-the-art techniques for Accelerated Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a
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Knowledge, Skills, and Abilities: PhD in Chemistry, Physics, Biophysics, Biology, Biochemistry or Structural Biology. Proven ability to optimize peptide, protein or nucleic acid crystallization systems. Basic
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year depending on performance and availability of funds. Candidates must have received a Ph.D. by the commencement of employment. BNL policy requires that after obtaining their PhD, eligible candidates
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scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) novel development of deep learning ML models and adaptation of existing ones