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Field
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functional theory (DFT) calculations of NMR parameters, powder X-ray diffraction, and other complementary analytical techniques. The postdoctoral researcher will be responsible for planning and conducting
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information, optimising how maintenance instructions and training materials are presented, and determining key parameters for improved maintenance. The expected outcomes are more efficient maintenance processes
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) Electroanalytical methods for determining key parameters in electrocatalysis, as well as gas Health, safety, and laboratory regulations Scientific English Technical Skills : Develop and optimize experimental
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 2 months ago
application to gas hydrate system to develop efficient key parameter estimation tools and large-scale 3D geologic model for gas hydrate reservoir. Learning opportunities will be given on the area of laboratory
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 6 hours ago
migration in porous media under in situ conditions, and • Machine learning application to gas hydrate system to develop efficient key parameter estimation tools and large-scale 3D geologic model for gas
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National Aeronautics and Space Administration (NASA) | Hampton, Virginia | United States | 22 days ago
the computational work. Researchers in the group have experience with crystal plasticity using finite element and fast Fourier transform methods, fatigue indicator parameters, process modeling and machine learning
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 6 hours ago
, increased storage) Apply geostatistical methods (e.g., kriging) to characterize the spatial heterogeneity of key reservoir parameters (e.g., porosity, permeability) and quantify subsurface uncertainty through
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Neural Nets (PiNNs) or “ML inspired” traffic models. PhD2 focuses on data assimilation and estimating start and boundary conditions such as path-flows, and other key parameters and inputs. In your role as
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focuses on hybrid traffic flow modelling such as Physics inspired Neural Nets (PiNNs) or “ML inspired” traffic models. PhD2 focuses on data assimilation and estimating start and boundary conditions such as
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) - Characterization of deposited films (morphology, homogeneity, optical response, adhesion, stability, etc.) - Correlation between formulation parameters, process conditions, and final device performance