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Field
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, multi- layered spatial data sets (e.g. Remote Sensing and GIS), and contributing to the growth of the interdisciplinary Integrated Coastal Studies Program, which focuses on issues related
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
of interest can be found at https://www.med.unc.edu/medicine/gi/education/basic-science-track/ Minimum Education and Experience Requirements PhD, MD or DVM or equivalent degree in related fields of study
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. Willingness to undertake extensive field work. On a regular basis, field work might require you to stay and work for several-day periods on Pulau Ubin. Desirable Skills Experience with using GIS software (e.g
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with disabilities. Location Omaha, NE Requisition Number: Staff_13880 Department Int Med GI 50001304 Business Unit College of Medicine Reg-Temp Full-Time Regular Work Schedule TBD by supervisor and
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, Python or related statistical software. GIS expertise is ideal. Application Process Please submit an online application and attach the following documents: Short statement of research interest. Complete CV
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application, demonstrated by relevant publications and research projects. Technical Skills: Proficiency in ecological modeling techniques, GIS (Geographic Information Systems), and statistical analysis tools
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hydro-climate studies. Strong background with GIS tools and spatial analysis techniques. Demonstrated expertise in climate variability assessment and the use of climate models. Experience with
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are an essential part of this job. Geospatial (remote sensing, GIS, & GPS) data acquisition (including DRONE data collection) and processing through advanced Data/Image Mining RFID-Transponder-based field data
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environmental sciences, conservation and biodiversity, ecology or equivalent Capacity to undertake environmental fieldwork Environmental data management and GIS experience Excellent written and verbal
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procedures used in spatial data analysis and vegetation dynamics modeling is required. The candidate should have extensive practical experience in the use of R and GIS. Demonstrated skills of Bayesian modeling