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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 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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technologies and methodologies, and Geographic Information Systems software (e.g., ArcGIS, QGIS), Python, and Matlab is required. Knowledge of other programming languages (e.g., C, C++, R, Javascript) is
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: · MATLAB · Python · ROS · Computer vision and/or YOLO · Pytorch and/or Tensor Flow · LiDAR · GIS · GNSS receivers Minimum Qualifications: Doctoral degree in Mechanical Engineering, Electrical Engineering, or
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proficiency in languages such as R and Python. Experience in GIS, remote sensing, and processing projected climate data. Proven ability to manage multiple tasks effectively, work collaboratively in team
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, Hydraulics, or related fields. Proficiency and experience in hydrologic and hydraulic modeling tools such as HEC-HMS, HEC-RAS, SWMM, InfoWorks, LISFLOOD, and SFINCS. Proficiency in GIS software (e.g., ESRI
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diverse soil and management conditions - Excellent technical knowledge of nitrogen dynamics and loss pathways in crop production - Proficiency in GIS and remote sensing workflows, using tools like QGIS, R
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learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with GIS software and Python, strong written
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tools: Tableau or PowerBI. Proficiency in at least one of the GIS tools: ArcGIS or QGIS. Advanced proficiency in at least one of the following programming languages: R or Python. Physical Requirements
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Postdoctoral Research Associate in Financial Accounting Gies College of Business Postdoctoral Research Associate Department of Accountancy We seek a full-time postdoctoral research associate to work
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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