Funded PhD Opportunities in GeoAI and Earth System Science for Carbon-Water Ecosystem Services
Start date: Fall 2027
Application deadline: December 31, 2026
Organization: UF/IFAS, University of Florida
Faculty contacts: Dr. Chang Zhao (
[email protected]) and Dr. Jiangxiao Qiu (
[email protected])
Job Description:
Drs. Chang Zhao and Jiangxiao Qiu are recruiting PhD students to conduct interdisciplinary research, integrating Earth observations, field and sensor observations, process-based biophysical modeling, and artificial intelligence to understand and predict carbon-water ecosystem services across spatial and temporal scales. Candidates will be reviewed from a shared pool and matched to the research group that best fits their expertise and interests, with opportunities for cross-lab collaboration and mentoring. This research is part of a recently NSF-funded project at the AI-geoscience interface (https://www.nsf.gov/awardsearch/show-award?AWD_ID=2615503).
Research Tracks:
Zhao Lab – Earth Observation and GeoAI: Satellite remote sensing and EO-derived biophysical products; multimodal geospatial data integration; machine learning and deep learning; hybrid AI-geoscience modeling of soil, water, and ecosystem processes; and spatiotemporal generalization, model interpretability, and uncertainty quantification. Key AI research interests include physics-informed AI, self-supervised learning, multimodal learning, geospatial foundation models, and active learning.
Qiu Lab – Landscape Ecology and Process-Based Modeling: Landscape ecology, ecosystem services, ecohydrology and biogeochemistry; process-based biophysical modeling; scaling and cross-scale interactions; ecosystem service interactions; and integration of machine learning and deep learning with process understanding for hybrid modeling and cross-scale prediction.
Candidate Profile:
Strong quantitative and computational skills, with training in geoinformatics, geospatial data science, remote sensing, computer science, computer engineering, environmental engineering, hydrological modeling, Earth system science, quantitative ecology, or a related field.
Substantive programming experience, preferably in Python and R, and quantitative modeling skills beyond routine GIS coursework or desktop GIS use.
Remote sensing and deep learning experience are important for the Zhao Lab; for the Qiu Lab, strong quantitative modeling and substantive programming experience (e.g., Python, R) and interest in AI-enabled hybrid modeling and knowledge-guided machine learning are highly preferred.
Interest in Earth system processes such as carbon cycling, vegetation dynamics, ecohydrology (e.g., evapotranspiration, streamflow, groundwater, soil-water interactions), or related ecosystem processes.
Ability to work with heterogeneous spatiotemporal datasets, develop reproducible computational workflows, communicate and collaborate across disciplines, and conduct independent research leading to peer-reviewed publications.
Application:
Please email both faculty (
[email protected] and
[email protected]) with your CV; a 1-2 page research statement describing your academic training, computational and quantitative experience, and research interests; unofficial undergraduate and graduate transcripts; and contact information for three references. In the statement, indicate your primary interest as Zhao Lab, Qiu Lab, or Both Opportunities. Links to code repositories, journal publications, or project portfolios are welcomed. Use the email subject "PhD Opportunities Fall 2027 - [Zhao / Qiu / Both]." Applications will be reviewed as they arrive.
University of Florida:
UF is an AAU land-, sea-, and space-grant university with strong interdisciplinary communities in AI, engineering, natural resources, and environmental science. Students have access to HiPerGator, UF's high-performance computing and AI infrastructure.