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rare-earth ions, with improved quality for optical, magneto-optical and laser applications. Different growth methods will be used such as Czochralski, Bridgman and Flux methods. Numerical modelling will
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North Carolina A&T State University | Greensboro, North Carolina | United States | about 24 hours ago
and quantitative methods with applications of local and regional food systems and to provide analytic expertise to expand the Department's research portfolio. This position is to provide support in
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the physical principles of structural dynamics and (vibro-)acoustics and the related numerical modeling techniques, such as the Finite Element Method (FEM), as well as numerical optimization, manufacturing
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Post-doctoral Researcher position. We seek an ambitious, well-trained, and collaborative scholar with a PhD in economics or a related field. Candidates must have expertise in experimental methods and
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...) and a sharp interest in numerical methods and high performance computing. Knowledge in statistical physics, solid state physics, theoretical/quantum chemistry or atomistic modeling. Serious usage LAMMPS
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an opportunity to contribute to a collaborative international research environment. GIANTS is a five-year theoretical and numerical modelling project focused on the late stage of terrestrial planet formation
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strongly correlated quantum systems. Experience (supported by peer-reviewed publications) with models and analytical or numerical methods/theories such as quantum mechanics, solid state physics, lattice
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to react to the wind before it hits the blades. Using upstream LiDAR measurements (taken several rotor diameters ahead), you will develop a wind field forecasting method, leveraging principles like Taylor’s
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numerical calculations (DFT...) to achieve the objectives of the project. Develop adequat numerical methods necessary to reach these goals. Study of the Pt/Co/HfO2 interface: - Calculate the thermodynamical
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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model