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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 14 hours 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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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 6 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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, dynamic mapping, mobile application development, spatial data analysis, visualization, and GIS. The Lab conducts interdisciplinary collaborative projects with research partners on campus at the UO, with
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relations, and environmental justice. Position Overview: The successful candidate will contribute to an innovative study that integrates GIS mapping, data analytics, policy analysis, and community-based
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: Position 1: Postdoctoral Associate – Software Engineering & Technical Development Proficiency in Python, geospatial libraries, API design (e.g., FastAPI), and cloud/HPC environments. Experience with
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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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undergraduate or graduate researchers. · Experience writing research grant applications. · Experience with software such as R, Python, Matlab, and GIS tools · Experience facilitating workshops
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: Experience with analyzing GPS tracks Good data-handling skills and ability to use R (compulsary) and preferably also Python and/or GIS competently Statistical/causal inference knowledge PhD degree in a related
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will lead the programming of R/Python packages for the analysis as well as adapt existing and develop new research methodologies and training materials. You will report research findings in the form
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programming and strong quantitative skills. Desirable Demonstrated knowledge of advanced biogeographic, comparative, and phylogenetic methods, quantitative methods in biodiversity studies, GIS in R, and spatial