Postdoctoral Research Associate: GeoAI and Remote Sensing for Invasive Species Ecology

Postdoctoral Associate

• 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.

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 GeoDI (Geospatial Digital Informatics) Lab.

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.

• 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.

• 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.

For full consideration, applications must be submitted online. Click on Apply Now at the top of this posting. 

A complete application includes (1) a letter (max 2 pages) of application summarizing the applicant's qualifications, interests, and suitability for the position, (2) a complete curriculum vitae, (3) a statement on research goals, and (4) a list of three references. After initial review, letters of recommendation will be requested from the references for selected applicants. 

Applications will be reviewed on a rolling basis starting immediately and will continue until the position is filled. The intended start date is flexible, ideally for the Spring 2026 semester. This is a full-time, 12-month appointment with the potential based on performance and funding availability.

Review of applications will be conducted on a rolling basis, with the first review beginning on November 15th.

All candidates for employment are subject to a pre-employment screening which includes a review of criminal records, reference checks, and verification of education. 

The selected candidate will be required to provide an official transcript to the hiring department upon hire. A transcript will not be considered “official” if a designation of “Issued to Student” is visible. Degrees earned from an educational institution outside of the United States require evaluation by a professional credentialing service provider approved by the National Association of Credential Evaluation Services (NACES), which can be found at http://www.naces.org/.