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Field
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Thermosphere models, analyzing simulation results, and performing data-model comparisons. Preferred Qualifications Special Conditions for Eligibility Department Information The Physics Department is
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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photoreceptors and hair cells, and potential treatments. We utilize mouse models and various approaches in molecular and cellular biology, biochemistry, omics, bioinformatics, electrophysiology, behavior, and
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. The selected candidate is expected to have expertise in one or more of the following areas: modeling contaminant flow and transport at various geospatial scales, process-based modeling of soil organic matter
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 3 days ago
is reproduced in the NASA Ames Electric Arc Shock Tube (EAST) facility, and is used to test models developed to predict the heating mechanisms. The primary models employed are the DPLR computational
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, mechanical evaluation, and biological testing. This includes formulating biocompatible and durable polymer composites, utilizing Fused Deposition Modeling (FDM) for precision fabrications, conducting fatigue
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. JOB DESCRIPTION AND POSITION REQUIREMENTS: We are searching for a multi-phase combustion computational fluid dynamics (CFD) Post-doctoral Scholar to join our Technology Concepts and Modeling Department
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surgical procedures, mouse behavior, extensive knowledge of the basal ganglia and striatum and amygdala. Experience with anterograde and retrograde circuit tracing. Experience with mouse models of compulsive
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assistants, and analyze the data they collect and prepare that data for publication and presentation at scientific meetings. This person will complete translation of an existing sheep model of chewing from a
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing