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Field
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the integration of hydroclimatic, geospatial, and remote sensing data to support decision-making, strengthen urban water resilience, and contribute to sustainable water resources management. The selected candidate
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, urbanization, and increasing water demand. The role involves the integration of hydroclimatic, geospatial, and remote sensing data to support decision-making, strengthen urban water resilience, and contribute
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geospatial analyses, producing hazard maps and decision-support tools to support surveillance and public health planning. Attends weekly laboratory meetings, relevant seminars, and occasional VectorEd team
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Research Assistant/Assistant Professor (Postdoctoral Researcher) for the project 2025/57/B/NZ8/03444
(ALS) data and multispectral imagery, methods for spatial data analysis and the use of GIS tools and geospatial workflows, working with environmental datasets, in particular those related to forest
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, training, and evaluating neural network models Experience applying machine learning to large geospatial, Earth observation, or environmental datasets Ability to work independently and take responsibility
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 7 hours ago
into practical tools, guidelines, and prototype implementations. Furthermore, the candidate may contribute to broader research efforts, including digital chart compilation workflows, geospatial data processing
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statistical and geospatial analyses (e.g., using ArcGIS, SPSS, Stata, Python, R). Conduct cross-city comparative research Map and analyse the geographical distribution of eviction patterns within and across
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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Archive, U.S. Forest Inventory and Analysis program, sPlotOpen) along with geospatial soil and climate data to apply resource colimitation theory in the refinement of correlative-based species distribution
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Digital Twin (FLDT) is a flagship research initiative funded by the UF Office of Research that leverages high-performance computing, AI, and geospatial modeling to build AI-powered replicas of the built and