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measure success. Basic Qualifications: A PhD in Materials Science and Engineering or a related field completed within the last 5 years Preferred Qualifications: Strong background in computational and image
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. One of the unique time-of-flight imaging capabilities enabled at VENUS is Bragg edge radiography. The measurement of the Bragg edge shift provides information of a lattice plane shift through a sample
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. Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to ORNLRecruiting@ornl.gov (For postdocs, use Postdocrecruitment
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) efforts within the Center for Nanophase Materials Sciences (CNMS) through hardware developments. As a postdoc, you will be expected to design and build custom hardware solutions focused on integrating
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priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs. Special Requirements: Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date
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: Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length
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their appointment. Preferred Qualifications: Experience with pan-genome datasets. Experience in plant stress/growth physiology and phenotyping, including extraction of relevant traits from image-based platforms
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degradation and lifetime assessment models for high temperature materials and coatings. As a postdoc, you will employ state-of-the-art technology, research equipment and computational resources to enable
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to ORNLRecruiting@ornl.gov (For postdocs, use Postdocrecruitment@ornl.gov ) with the position title and number referenced in the subject line. Instructions to upload documents to your candidate profile: Login
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grid analytics, and scientific imaging. The successful candidate will design and implement sparse algorithms for large-scale scientific and numerical computations. This role offers an exceptional