Post Doctoral Researcher

About the role

A post-doctoral researcher position is available for a NASA -funded project focused on agroforestry, precision agriculture, and environmental monitoring using Earth observation, GIS , remote sensing, and artificial intelligence (AI).

Responsibilities

• Design and implement field sampling protocols for agroforestry systems, forest farming, silvopasture, alley cropping, and other natural resource management projects.
• Conduct field sampling and environmental data collection for agroforestry and natural resource projects.
• Acquire, process, and analyze remotely sensed datasets including NASA Earth observation products, Landsat, Sentinel, LiDAR, UAV imagery, NAIP imagery, hyperspectral data, and other geospatial datasets.
• Develop predictive models, maps, and decision-support tools for sustainable land management.
• Publish research, assist with grant writing, present findings at scientific meetings, and mentor students.
• Support outreach, stakeholder engagement, and collaboration with NASA and university partners.

Requirements

• Experience processing and analyzing satellite imagery, UAV imagery, LiDAR data, or other geospatial datasets.
• Proficiency in statistical analysis and programming using R, Python, or similar software.
• Knowledge of spatial statistics, predictive modeling, machine learning, or AI applications for environmental or agricultural systems.
• Effective demonstrated quantitative, analytical, and communication skills.

Qualifications

• Ph.D. in agronomy, soil science, horticulture, agroforestry, forest biometrics, quantitative, silviculture, forest ecology, geography, remote sensing, or related natural resources management discipline.
• Preference will be given to candidates with experience in one or more of the following: Agroforestry systems, including alley cropping, silvopasture, forest farming, riparian buffers, or windbreaks.
• Experience with forest inventory, vegetation mapping, ecological modeling, or ecosystem service assessment.

Skills

• Experience processing and analyzing satellite imagery, UAV imagery, LiDAR data, or other geospatial datasets.
• Statistical analysis and programming proficiency using R, Python, or similar software.
• Knowledge of spatial statistics, predictive modeling, machine learning, or AI applications for environmental or agricultural systems.
• Quantitative, analytical, and communication skills.

Benefits

Tennessee State University offers eligible employees a competitive benefits package that includes annual leave (vacation), sick leave, medical, dental, vision, life insurance, retirement, flexible benefits plans and more.