Job Description:
The Geospatial Artificial Intelligence (GeoAI) Lab at the University of Florida, led by Dr. Di Yang, is seeking a highly motivated Postdoctoral Research Associate to join a new, multi-institutional research project focused on the invasive grass Ventenata dubia (VEDU). This project is a collaboration with leading experts at the University of Montana (Spatial Analysis Lab) and Boise State University.
The successful candidate will lead the development and implementation of cutting-edge remote sensing and machine learning techniques to address critical questions about invasive species surveillance and invasion dynamics. Key research themes include: 1) characterizing invasion resistance, 2) assessing the role of phenotypic plasticity in its competitive success, and 3) developing robust methods for spectral phenotyping using ground, drone, and satellite-based sensors. This position offers a unique opportunity to work at the intersection of remote sensing, spectranomics, genetic analysis, GeoAI, and invasion ecology within a dynamic, collaborative team.
Responsibilities:
- Design and lead remote sensing data acquisition campaigns using multi-scale platforms, including ground-based spectrometers, UAVs (optical, Lidar), and satellite imagery (e.g., Planet, Sentinel, Landsat).
- Develop and apply advanced machine learning and deep learning models (GeoAI) for fusing, analyzing, and interpreting multi-sensor data to track invasion species patterns
- Create novel analytical workflows to build calibration equations for discriminating VEDU from other co-occurring grass species.
- Integrate remote sensing-derived products with in-situ ecological data (e.g., canopy cover, height, alpha diversity, chemistry, soil texture, disturbance intensity) to model invasion dynamics and resilience across landscapes.
- Collaborate closely with project partners to synthesize findings and build follow-on funding opportunities.
- Lead the preparation of high-impact, peer-reviewed publications.
- Present research findings at national and international scientific conferences.
- Mentor graduate and undergraduate student in the .
UF is the state’s oldest, largest, and most comprehensive land grant university with an enrollment of over , students and was ranked 7th in the country among public universities (US News and World Report 5 rankings), and 1st among public institutions in the Wall Street Journal 3 survey. UF is located in Gainesville, a city of approximately , residents in North-Central Florida, miles from the Gulf of Mexico, and miles from the Atlantic Ocean, and within a 2-hour drive to large metropolitan areas (Orlando, Tampa, Jacksonville). The beautiful climate and extensive nearby parks and recreational areas afford year-round outdoor activities, including hiking, biking, and nature photography. UF’s large college sports programs, museums, and performing arts center support a range of activities and cultural events for residents to enjoy. Alachua County schools are highly rated and offer a variety of programs including magnet schools and an international baccalaureate program. Learn more about what Gainesville has to offer at . Expected Salary:
The salary is competitive and commensurate with qualifications and experience, and the compensation includes a full benefits package. To see more, visit, benefits.hr.ufl.ed.
Required Qualifications:
- A Ph.D. (by the start date) in Remote Sensing, Geography, Biology, Geospatial Science, Environmental Science, Ecology, or a closely related field.
- Demonstrated expertise in processing and analyzing remote sensing data (hyperspectral and/or Lidar is a strong plus).
- Strong proficiency in programming, particularly in Python and GEE for geospatial analysis and data science.
- Experience with machine learning/deep learning frameworks (e.g., PyTorch, TensorFlow) applied to image or geospatial data.
- A track record of first-author publications in peer-reviewed journals.
- Excellent communication, collaboration, and writing skills.
Preferred:
- Experience in plant ecology, invasion science, or agronomy.
- Specific expertise in reflectance spectroscopy and chemometrics for vegetation analysis or high-throughput phenotyping.
- A strong background in GeoAI, computer vision, and data fusion techniques.
- Experience designing UAV-based remote sensing campaigns.
- Experience leading ground-based vegetation surveys.
- Demonstrated ability to work effectively in a collaborative, interdisciplinary research team.
