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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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We are seeking a Postdoctoral Researcher in Geospatial Urban Big Data to support the analysis of urban dynamics and decision-making in territorial planning. The candidate must be proficient in GIS
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focused on developing full-chain health impact assessment models to assess transportation planning and policy decisions in major US cities, for example the adoption of electric cars and electric bikes
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 5 hours ago
communication skills Ability to work in a team setting and be self-motivated Other Requirements: Established publication record in peer reviewed journals Preferred Qualifications: Experience with GIS, UAV data and
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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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Geospatial Carbon Registry external link (OGCR). The project is aimed at quantifying and mapping carbon storage potential and future carbon removal and carbon farming policy scenarios, integrating process
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storage potential and future carbon removal and carbon farming policy scenarios. It integrates process-based knowledge with machine learning and especially aims to spatially quantify uncertainties. You will
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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
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sensor data, public databases, and GIS. Predictive Modeling:Design predictive models to evaluate the impact of urban and environmental policies on public health. Interdisciplinary Collaboration:Collaborate
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Geospatial Carbon Registry external link (OGCR). The project is aimed at quantifying and mapping carbon storage potential and future carbon removal and carbon farming policy scenarios, integrating process