59 application-forms-"https:" "https:" "FORTH" Postdoctoral positions at Oak Ridge National Laboratory
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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to commit to ORNL’s Research Code of Conduct. Our full code of conduct and a statement by the Lab Director’s office can be found here: https://www.ornl.gov/content/research-integrity Basic Qualifications
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Requisition Id 16197 Overview: We are accepting applications for a Postdoctoral Research Associate position who will focus on the intersection of material science, materials characterization, and
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applications. You will utilize ORNL's new capabilities of aberration-corrected scanning transmission electron microscopy (STEM). A particular focus of the research will be understanding heterogeneities, such as
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performance modeling, static analysis, or PIM/heterogeneous architecture research. Knowledge of large-scale scientific computing applications and algorithms (sparse linear system solvers, dense matrix
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understanding of ferroelectrics, especially in thin film form. Experience in Python and/or Matlab. Experience in materials characterization techniques such as Raman, X-ray diffraction, and scanning electron
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of advanced materials. Research efforts will include the application of density functional theory packages and in-house codes, and the development of supplemental numerical tools, to describe
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challenges and conduct the research needed to accelerate the delivery of solutions to the marketplace. The Radiation Transport and HPC Methods (RTHPCM) Group within the Nuclear Applications Methods and Data
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tasks within deadlines, and adapt to ever changing needs. Special Requirements: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all
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on using neutron scattering to probe the structure and dynamics of materials relevant to energy applications and/or structural materials. In particular, the work will aim to (1) understand how hybridized