In A Nutshell
The School of Geographical Sciences and Urban Planning is seeking up to two (2) highly motivated Postdoctoral Scholars in Geospatial Artificial Intelligence (GeoAI) under the supervision of Dr. Wenwen Li. The successful candidates will work at the intersection of cutting-edge AI (e.g., computer vision, generative AI, large language models, multimodal AI) and geospatial applications (e.g., environmental monitoring and forecasting using time-series satellite imagery, and linking multimodal geospatial information for comprehensive question answering and information retrieval). Candidates are expected to contribute to both the methodological innovation and societal impact of GeoAI.
Responsibilities
- Develop, train, and fine-tune cutting-edge AI and GeoAI models for environmental monitoring and the analysis of complex spatiotemporal phenomena.
- Integrate multimodal geospatial data (e.g., satellite, in-situ, and social data) to advance understanding of Earth system processes.
- Publish research results in high-impact journals and present findings at major international conferences.
- Collaborate with interdisciplinary teams across Earth science, computer science, and sustainability domains to support project goals and proposal development.
- Mentor graduate and undergraduate students, fostering collaboration and research excellence.
- Contribute to the development of open-source tools, datasets, or workflows that advance the broader GeoAI research community.
Skillset
Required Qualifications:
- Ph.D. or terminal degree in Computer Science, Remote Sensing, GIScience, or a closely related field by the time of appointment.
- Strong background in deep learning, computer vision, or generative AI.
- Demonstrated experience with one or more deep learning frameworks (e.g., PyTorch, TensorFlow).
- Demonstrated ability to work with large-scale Earth observation data and apply AI methods for geospatial big data analysis.
- Record of peer-reviewed publications in relevant fields.
Desired Qualifications:
- Demonstrated experience with GeoAI methods, including multimodal learning, foundation models, large language models (LLMs), and/or vision-language models (VLMs).
- Familiarity with frameworks such as PyTorch, Hugging Face Transformers, and PEFT, with proficiency in multimodal learning, transformer-based architectures, and applying these models for spatiotemporal reasoning and geospatial understanding.
- Proven ability to work with geospatial and remote sensing data, including data processing and analysis using tools such as GDAL, Google Earth Engine, or similar platforms, and working in cloud or high-performance computing (HPC) environments.
- Demonstrated experience and understanding of spatiotemporal modeling, AI-driven environmental mapping, or forecasting applications.
- Excellent written and oral communication skills, and a demonstrated ability to work collaboratively in interdisciplinary research teams.
- Demonstrated commitment to working with faculty, staff, students, and communities to advance the principles of the ASU Charter.