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, Organization of field trips, data collection and lab work, Spectral data analysis, data processing, and model development, ‘R’ or Python programming, Co-supervise PhD and undergraduate students. Be willing to be
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to agricultural water management, through key research tasks such as conducting fieldwork, contributing to land and irrigation suitability mapping, irrigation design and management, processing and analysis of data
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, and academic institutions. Required Qualifications PhD in one of the following fields: smart urban systems engineering, computer science applied to urban infrastructure, data science, or a related field
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physiology, or related disciplines. Experience with remote sensing data analysis techniques for agricultural applications is preferred. Experience with climate data analysis and crop modeling techniques is
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: Mathematical Modeling: Develop mathematical models to simulate and optimize the entire waste management chain—from collection to treatment. Data Integration and Analysis: Integrate real-time and historical data
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Qualifications: PhD in Geography, Urban Planning, Urban Sociology, Public Policy, or a related field. Expertise in social and economic geography to study territorial disparities. Understanding of key concepts
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Sciences, or related fields. Strong background in isotope hydrology, ecohydrology, and modeling techniques. Experience in fieldwork, data analysis, and scientific writing. Excellent communication skills and
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of soil health and nutrient acquisition Proficiency in Microsoft Office, data analysis using R and SAS. Basic knowledge of remote sensing application to quantify carbon sequestration is desirable
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databases Conduct ethnobotanical field surveys Elaborate atlas botanical book including design and content Data scheduling, collection, analysis, interpretation, and presentation. Supervise PhD and Master
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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