Postdoctoral Research Associate in Geospatial AI (GeoAI) and For

LINCOLN UNIVERSITY • Jefferson City, MO

5d • Hybrid

About The Position

The Postdoctoral Research Associate will engage in research and development of a GeoAI-powered early warning system for forest health by integrating multi-source geospatial data, including satellite imagery, UAV-based LiDAR and multispectral data, and environmental datasets. This position supports a USDA-NIFA funded project focused on detecting early indicators of forest stress, pest infestation, and environmental disturbances using advanced artificial intelligence and geospatial analytics. The role contributes to research, education, and extension activities in Missouri and supports the broader mission of advancing innovation in geospatial science and environmental monitoring.

Requirements

• Ph.D. in Geospatial Science, Geography, Remote Sensing, Data Science, Forestry, Environmental Science, or a closely related field.
• Valid driver's license.
• Must have or be able to obtain a Remote Pilot Certificate (FAA Part 107).
• Demonstrated experience conducting independent research.
• Ability to manage research timelines and deliverables within a grant-funded project.
• Strong understanding of Geospatial Artificial Intelligence (GeoAI), including integration of machine learning and deep learning methods with geospatial and environmental datasets.
• Proficiency in programming languages such as Python or R, including experience with relevant libraries for data analysis, modeling, and visualization.
• Knowledge of spatial data processing, geostatistics, and remote sensing techniques.
• Familiarity with multi-source data integration and spatial modeling workflows.
• Experience working with geospatial software and tools such as GIS platforms, remote sensing tools, and data processing frameworks.
• Ability to interpret scientific data and translate findings into actionable insights.
• Strong analytical, problem-solving, and critical thinking skills.
• Effective written and verbal communication skills for technical and academic audiences.
• Ability to work both independently and collaboratively within interdisciplinary research teams.
• Strong organizational skills and ability to manage multiple tasks and deadlines.

Nice To Haves

• Experience working with UAV or LiDAR data for environmental or forestry applications.
• Background in applying machine learning methods to geospatial or ecological datasets.
• Demonstrated record of peer-reviewed publications or scientific research dissemination.
• Ability to work independently and manage projects with minimal supervision.
• Strong organizational and problem-solving skills, particularly when working with large or complex datasets.
• Experience collaborating across interdisciplinary teams.

Responsibilities

• Plan and implement research activities focused on early detection of forest stress, disturbance, and ecological change using geospatial analytics and artificial intelligence.
• Compile, collect, clean, and process geospatial and ancillary datasets from multiple sources, including satellite imagery, UAV-based LiDAR, multispectral imagery, and environmental data.
• Develop, train, and optimize GeoAI models using machine learning and deep learning techniques for spatial analysis and predictive modeling.
• Validate GeoAI models through field verification and collaboration with the Missouri Ozark Forest Ecosystem Project (MOFEP).
• Develop decision-support tools and interfaces that translate complex geospatial outputs into usable information for stakeholders.
• Contribute to peer-reviewed publications, conference presentations, and technical documentation required for project deliverables.
• Mentor graduate and undergraduate students involved in research activities.
• Collaborate with interdisciplinary teams across research, extension, and education initiatives.
• Maintain accurate records of research activities, methodologies, and results.
• Perform other duties as assigned by the supervisor in support of project goals.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree