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large remote sensing datasets (e.g., Landsat, Sentinel-2, MODIS) and spatial datasets (e.g., FACTs, FTEM, field data) both locally with R/Python and via Google Earth Engine for wild-treatment outcome
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mapping. The postdoc will work as part of a larger, interdisciplinary research team to explore the connections between flood exposure, environmental pathogen prevalence, and human health. Job Description
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-resolution shear wave velocity maps across the accretionary wedge and oceanic plate. The successful candidate will be based in the UW FiberLab (https://fiberlab.uw.edu ) under the supervision of Brad Lipovsky
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-built behavioral assays to map the neural dynamics of social behavior. Our comprehensive approach maps intricate neural circuits from the cellular level to neural circuits and behavior, thereby uncovering
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-development and refinement of conceptual models; devising management scenarios; building network models in one or more platforms (e.g., loop analysis/qpress; fuzzy cognitive maps/Mental Modeler; Bayesian belief
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maps, and evaluate how forest management strategies can mitigate multi-hazard risks. The postdoc will also support project workshops, scientific communication, and the development of management
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agricultural transformation and small-scale producers. Priority skills are proficiency with Stata, R and spatial mapping, and familiarity with the LSMS-ISA and other agricultural and spatial data in SSA and SA
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to achieve the following objectives: 1. Characterize 3-D Urban Structure and Change: Utilize data from multiple remote-sensing platforms and deep learning algorithms to generate high-resolution maps of 3-D
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scenarios; building network models in one or more platforms (e.g., loop analysis/qpress; fuzzy cognitive maps/Mental Modeler; Bayesian belief networks; etc.); and interpreting, communicating to broad