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aligned with the aims of the Addiction & Decision Neuroscience Lab (ADN). Current work focuses on cognitive modeling of decision-making in both laboratory tasks and real-world settings, as well as machine
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models using simulation and experimental data to predict material properties and guide further study. Document datasets, benchmark performance, and interpret results for validation and follow-up analysis
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models using simulation and experimental data to predict material properties and guide further study. Document datasets, benchmark performance, and interpret results for validation and follow-up analysis
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and evolution of Digital Twin Earth by undertaking collaborative research across a range of domains and advancing the use of EO for enhanced simulations, predictions and what-if scenarios. Supporting
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spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational methods to discover unknown metabolites that have strong associations to experimental
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data-model integration, leveraging the U.S. Department of Energy’s (DOE) Leadership-Class Computing Facilities to advance predictive understanding of complex environmental systems. Major Duties
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materials such as anisotropic, hyperbolic, epsilon-near-zero, dispersive, lossy or active media. The successful candidate will develop analytical models and numerical tools, generate original research
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, mathematics, computer science, engineering or a related discipline Required Other None Additional Preferred Experience working in one of the following areas: Machine learning/predictive modeling
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geophysical monitoring, data integration, and the development of predictive models and visualization tools. Candidates should have a PhD in geophysics, hydrology, environmental engineering, or a related
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National Aeronautics and Space Administration (NASA) | Huntsville, Alabama | United States | 12 days ago
. Description: The NASA Short-term Prediction Research and Transition (SPoRT) Center at Marshall Space Flight Center is an end-to-end research-to-applications/application-to-research project with a focus in