The Department of Agricultural & Environmental Sciences within the College of Agriculture at Tennessee State University is seeking a Post-doctoral Research Associate in forest management and modeling to participate in a research project that aims to model the forest ecosystem characteristics, dynamics, and values across the multi-functional forest landscapes. The project will utilize plot-based forest inventory data from private and public forestlands, remote sensing data from Landsat and Sentinel, airborne LiDAR (Light Detection and Ranging) point cloud data, and NAIP (National Agriculture Imagery Program) imageries; and other GIS data.
The post-doctoral research associate position will be a full-time position, which requires traveling to the field frequently, interacting with stakeholders, spend time acquiring, processing, and analyzing a large array of geospatial and forestry data sets, and writing manuscripts for publication in academic journals. Specific responsibilities include but not limited to are:
Develop field sampling plan and protocol
Establish temporary sample plots, collect, collate and compile field data
Acquire, process and analyze remotely sensed and GIS data
Develop and maintain hierarchical databases
Conceptualize, develop, validate and parameterize empirical predictive models
Disseminate research results, with emphasis on peer-reviewed publications
Participate project meetings and workshops
Present research results and findings at professional meetings and conferences
Education/Training: a Ph.D. in forest science, forest biometrics, quantitative silviculture, forest ecology, or related natural resources management discipline with working knowledge, background and experience in forestry/ecology, GIS, remote sensing, and quantitative methods (e.g., statistical modeling and machine learning) are required. The candidate for this position should have experience in processing LiDAR and remote sensing imagery and incorporating these spatial data into forest modeling applications. Preference will also be given to candidates that possess a demonstrated record of scholarship in publishing manuscripts, and experience in data analysis and use of statistics to develop model as a predictive tool. The position will require essential working knowledge or background in forestry/ecology, GIS, forest inventory, remote sensing, and statistical data analysis in R or SAS or any other statistical software environment.