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Field
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skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models). Knowledge of any of the following will be an advantage: geo-statistics, AI
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for the materiality of the built environment in defined regions, based on MFA and supported by BIM, GIS, IoT and AI technologies. Map existing anthropogenic material stocks and their dynamics and simulate circularity
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 15 hours ago
effective manipulation of gridded data, such as MATLAB, Python, R, or NCO. Ability to read and do limited modifications of C/C++ model source code. Ability to use Windows or MacOS. Ability to run simulations
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for the materiality of the built environment in defined regions, based on MFA and supported by BIM, GIS, IoT and AI technologies. Map existing anthropogenic material stocks and their dynamics and simulate circularity
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modelling, including experience in using a programming language suitable for geospatial data analysis (e.g., R, Python). Experience in applying remote sensing methods, for instance in ecological, geophysical
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: Strong interest in community engagement, data science, or social impact Experience with data analysis tools (e.g., Excel, R, Python, GIS, etc.) is a plus Excellent written and verbal communication skills
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on conventional GIS visualization. It centers on building rigorous, high-fidelity, georeferenced 3D environments that serve as the structural backbone for analytics, simulation, and next-generation interactive
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supervision The following experience will strengthen your application: Advanced coding skills (Python, R, etc.) Expertise in GIS and data visualisation. Experience applying Machine Learning, particularly
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guidance to support staff and/or student employees. Must be proficient in the use of python, JavaScript, CSS, and HTML; PostgreSQL, MySQL, and SQL oracle; Linux Red Hat and Ubuntu ****The final salary
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/statistical skills Experience with statistical analysis software (e.g. R, SAS), GIS software (e.g., ArcGIS, QGis), and programming languages (e.g., R, Python, C++). Experience or interest in science integration