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 .

• 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 applicants 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 Assessment Required: