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properties in drylands worldwide and analyzing their spatial distribution. The research will utilize innovative remote sensing techniques, advanced modeling frameworks, and cutting-edge datasets to enhance our
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international users in water and food systems. Job Description: Over the past few years, advances in computational power and the increasing availability of large volumes of remote sensing data with finer spatial
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techniques. Proficiency in R, Python, or MATLAB for data processing, geospatial analysis, and statistical modeling. Experience with time series analysis, spatial mapping, and oceanographic data interpretation
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including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity. Pay Range: $74,128 - $75,611 Postdoctoral Scholarship - Cancer Biologist in Tumor Spatial
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research. Interests and/or background in Arctic research, hydrological modeling, surface water fieldwork, community engagement, and/or spatial statistics are also a plus. PROJECTED START DATE There is
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. Develops advanced analytical models and systems and provides solutions and analyses to support strategic and tactical decisions. Adheres to University and unit-level policies and procedures and safeguards
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applicant must have professional proficiency in English (written and spoken). Desirable qualifications: Prior experience in working with economic models of migration, residential choice models, and/or spatial
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efforts to model nitrous oxide emissions reduction. Qualifying skills: We seek a candidate with experience working with quantitative and/or spatial data, including familiarity with programming environments
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research, including such topics as microclimate, watershed management and modeling, sediment analysis, soil formation, water scarcity and security, geomorphology, land cover change, application of remote
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the use of R and/or Python Basic understanding of statistical modeling, and machine learning Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA