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datasets, and synthesize results for publication and reporting. Proficiency in programming languages and software commonly used in research, such as Python, MATLAB, R, or GIS tools. Excellent written and
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 44 minutes ago
geospatial data processing and python programming. Candidates should also have knowledge of optical, lidar, and ground penetrating radar sensing systems and understanding of pavement structures and condition
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health, AI and GIS, food and nutritional security, and occupational health and safety. EGH programs offer a Master of Health Science and doctoral degrees in Environmental Health and One Health. The College
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and offsite data and manage them in appropriate formats and structures relative to spatial and other quantitative data. Ability to use GIS packages such as ArcMap or QGIS. Production of high-quality
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geology, and geodesy Good field expertise in active tectonics and paleoseismology; knowledge of Balkans/Aegean geology is a plus Experience with structural/kinematic analysis and GIS Strong communication
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modelling, and real-time graphics programming. Proficiency in programming languages such as C++, C#, Python, or JavaScript. Strong understanding of UX/UI principles for immersive experiences. Experience in
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for simulating river network dynamics, such as R, Julia, Python, or GIS-based hydrological modeling platforms. Ability to integrate physical, chemical, and biological components into the river-lake network models
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limited to a record of scholarly publications Experience in programming and code development (python, R) Proven ability to publish manuscripts in peer-reviewed scientific journals Fluent English, both
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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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expertise in forest ecology, disturbance ecology, and landscape ecology, and methodological expertise in harmonizing distinct databases (e.g., forest inventory, remote sensing, land cover), GIS, and R-based