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PhD degree related to geography, geographic information systems, remote sensing, or physical / biological / environment / natural resource discipline with substantive coursework in geospatial analysis and modeling, and one (1) year relevant work experience.
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Our areas of expertise include remote sensing, geospatial analysis, GIS, soil science, water quality and demand evaluation, agricultural systems, salinity and nutrient management, ecosystem restoration, statistics, and regulatory policy.
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Experience with drone operation, photogrammetry, remote sensing, big data management and processing, FAA Part 107 pilot certification a plus. This position will report to the GIS Department Manager but will regularly support project managers in other departments including NEPA, hydrology, biology, air quality, and environmental remediation.
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The successful Remote Sensing Scientist candidate will be responsible for working with our growing geospatial team comprised of remote sensing/GIS experts and a range of agronomic, environmental, and other land-based science disciplines.
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Applicants must have strong analytical, remote sensing and GIS skills and an exceptionally strong understanding of machine learning algorithms. Work in a team environment on a wide range of projects, supporting our team of scientists, remote sensing analysts, and GIS analysts.
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Land IQ, LLC is seeking a Remote Sensing Scientist in its Sacramento, California office with a specialization in data driven analytics and algorithm development with digital images for agricultural and other land use applications (crop classification, yield modeling, land use impacts, land use change, etc.
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Remote sensing-based land cover mapping over agricultural areas and crop classification; Develop and implement remote sensing and statistical methodologies to perform land use and land cover classification (crop classification.
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Remote sensing of crop evapotranspiration; and/or. BS/MS Remote Sensing/Geography/Data Sciences field (advanced degree preferred) Proficient at remote sensing modeling (e.g., data cleaning, feature selection, analysis, designing, building, and model assessment.
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Familiar with deep learning platforms (e.g., Keras, Tensorflow) and related classification and segmentation algorithms. Proficient at machine learning algorithms, familiar with algorithms like Generalized Linear Model, Random Forest, CART, and/or deep learning.
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Perform and develop advanced object-based image analysis procedures and methodologies for earth science applications. Leverage a strong understanding of multispectral imagery characteristics to solve complex agricultural and environmental land-based problems.
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Additionally, familiarity with agricultural land management tools such as DNDC (DeNitrification-DeComposition), DAYCENT, remote sensing, GIS (Geographic Information Systems), and knowledge of soil science will be considered a plus.
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Develop and perform raster-based imagery analysis procedures, spatial and statistical modeling applications. Image based time series analysis and crop phenology modeling; Resourceful in seeking, preparing, and/or creating raster & vector data.
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Understand geospatial challenges and conceptualize and articulate analysis approach ideas within a multi-disciplinary team. Image based crop yield modeling. Land IQ specializes in providing solutions to challenging agricultural and environmental problems throughout the western United States.
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Develop innovative image analysis solutions using a wide array of data sources. Experienced in Python packages like NumPy, SciPy, Geopandas, Matplotlib, Scikit-Learn, Keras. This is an environmental consulting position requiring ability to develop analysis approaches/methodology and work within a team to optimize methods.
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Proficient at advanced spatial analysis and geoprocessing (e.g., image segmentation, object-based image analysis). Enthusiastic about collaborating with team members to reach team and organizational goals.
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