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Field
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research to incorporate disturbance through time and remote sensing into a scaling framework of forest structure and functional diversity using in-situ and remotely sensed data, as well as assisting with
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make a difference in the world! Position Information Texas A&M AgriLife Research at Temple is seeking a highly motivated scientist with expertise in areas of crop modeling, remote sensing, geospatial
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project and will be genuinely excited to collaborate—both within the lab and with our broader network of partners. They will help strengthen the supportive culture of the lab, which values rigor, humor, and
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: The scholar will lead research projects linked to detecting large-scale ocean biogeochemical changes using a combination of remote sensing, modeling and in situ observations. Specifically, the postdoc will use
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experience in Python, C++, MATLAB, or LabVIEW. Brief Description of Duties (Postdoctoral Associate): The selected candidates will join the Atmospheric Remote Sensing Group , a leader in innovative multi-sensor
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transfer. Fire observation systems. Interpretation of remote sensing data. Feather characterization. Modeling and automation of workflows. Robust processing of field measurements. Professional Experience
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interventions. Flexible hybrid working is possible: data analysis and statistical work can be done remotely, with regular on-site meetings for team collaboration and integration with clinical and imaging experts
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of solution platforms for integrating spatial and temporal information, harnessing remote sensing data, using climate information, understanding fuel accumulation and running physics-based or data-driven
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data for urban characterization. The work includes developing algorithms, performing large-scale analyses, and collaborating with partners across disciplines in remote sensing, urban studies, and climate
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articles, and supervising graduate students, if any. 6. Performs other duties as assigned. Required Education: A PhD degree in a field related to the tasks above within the broader theme of remote sensing