The Spatially Explicit Artificial Intelligence (SEAI) Lab at the University of Texas at Austin, led by Dr. Gengchen Mai (Assistant Professor, Department of Geography and the Environment), is recruiting 2–3 PhD students to begin in Fall 2026. The lab focuses on cutting-edge research in Geospatial Artificial Intelligence (GeoAI), including spatially explicit machine learning, deep learning, knowledge graphs, and spatial data mining. Students will have opportunities to collaborate with leading researchers from academia and industry, including Google, Google Research, OpenAI, and Esri, and may intern at high-tech companies. The University of Texas at Austin is a top-ranked public research university. Admitted PhD students will be funded through National Science Foundation grants, NSF PhD Fellowships (some requiring US citizenship or permanent residency), and UT Geography Teaching Assistantships. Applicants should hold a bachelor's or master's degree in GIScience, remote sensing, computer science, information science, or a related field by August 2026, have a passion for GIScience and GeoAI, be familiar with programming languages (Java, C++, Python), and spatial analysis tools (ArcGIS, QGIS). Research experience in machine learning and AI is a plus but not required. English fluency is required. To apply, send your CV and a brief statement to Dr. Mai at [email protected]. For more information, visit https://gengchenmai.github.io/.

The Spatially Explicit Artificial Intelligence (SEAI) Lab at the University of Texas at Austin, led by Dr. Gengchen Mai, is recruiting 2-3 PhD students to begin in Fall 2026. The lab specializes in Geospatial AI (GeoAI), spatially explicit machine learning, deep learning, knowledge graphs, and spatial data mining. Students will collaborate on cutting-edge projects with academic and industry partners, including Google, Google Research, OpenAI, and Esri. The University of Texas at Austin is a top-ranked public research university, and Dr. Mai is an Assistant Professor in the Department of Geography and the Environment, with extensive experience in both academia and industry. Admitted students will be fully funded through NSF grants, fellowships, and teaching assistantships, with opportunities for industry internships. Applicants should have a background in GIScience, remote sensing, computer science, or related fields, proficiency in programming and spatial analysis tools, and strong English skills. To apply, send a CV and brief statement to Dr. Mai. For more information, visit the lab's recruitment page.