Position Details Position Information
Recruitment/Posting Title Post Doc Associate
Department SEBS - Remote Sensing
Salary Commensurate With Experience
Posting Summary The Center for Remote Sensing & Spatial Analysis is seeking a Post Doctoral Associate who will be responsible for providing expertise in ecological applications of remote sensing and geospatial analysis, working collaboratively with the faculty and staff of the Center For Remote Sensing & Spatial Analysis; the Department of Ecology, Evolution, and Natural Resources, the School of Environmental and Biological Sciences, and across the University. This position will be expected to perform both quantitative, geospatial, and qualitative research and data analysis, write technical reports and journal article, and prepares conference presentations as well as briefings with key constituencies. This position will be involved with two main projects: Remotely sensed classification, mapping, and analysis of spatial/temporal trends of New Jersey's seagrass habitats. Remotely sensed characterization of Pinelands forest structural composition and the light/temperature environment and consequences for fence lizard habitat quality.
Position Status Full Time
Posting Number 23FA0754
Posting Open Date
Posting Close Date
Qualifications
Minimum Education and Experience A PhD degree in ecology, natural resources management, geography or related field with advanced training and/or professional experience or certifications in Geospatial Information Science and Technology. Extensive experience in the development and application of remote sensing and geospatial information sciences to ecology and/or wildlife biology/conservation. Experience integrating knowledge and methods from different disciplines; and working on interdisciplinary teams.
Certifications/Licenses
Required Knowledge, Skills, and Abilities Expertise in remotely sensed image analysis and classification working with both multispectral and LiDAR sensor data, especially as it relates to characterizing the vegetation community composition and structure and relation to habitat quality.
Firsthand knowledge of ecological field inventory, both plant and animal, techniques. Demonstrated coding skills in either RStudio or Python. Demonstrated experience with ERDAS Imagine or other remotely sensed image analysis software, ESRI ArcGIS/ArcPro, machine learning/statistical analysis in R Strong communication skills with excellent knowledge in English, both written and spoken.
Equipment Utilized
Physical Demands and Work Environment Most of the work will be conducted indoors in a computer lab but there will be some field work on coastal bays, salt marshes and forests. Field work can be physically demanding/fatiguing with hot/humid weather, biting insects and some physical discomfort.
Overview