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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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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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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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Leverage a strong understanding of multispectral imagery characteristics to solve complex agricultural and environmental land-based problems. Land IQ specializes in providing solutions to challenging agricultural and environmental problems throughout the western United States.
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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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Familiar with deep learning platforms (e.g., Keras, Tensorflow) and related classification and segmentation algorithms. Applicants must have strong analytical, remote sensing and GIS skills and an exceptionally strong understanding of machine learning algorithms.
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Proficient at machine learning algorithms, familiar with algorithms like Generalized Linear Model, Random Forest, CART, and/or deep learning. 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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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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Perform and develop advanced object-based image analysis procedures and methodologies for earth science applications. Develop and perform raster-based imagery analysis procedures, spatial and statistical modeling applications.
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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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Education: BS/MS Remote Sensing/Geography/Data Sciences field (advanced degree preferred) Remote Sensing Scientist. 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. 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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Strong organizational, communication and writing skills, positive/enthusiastic attitude, passionate about professional pursuits, personable, ability and desire to learn, and attention to detail, strong moral and ethical personal standards.
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land job Title: project engineer Company: General Atomics
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