Assistant Professor in GeoAI and Spatial Data Science

Assistant Professor in GeoAI and Spatial Data Science - Binghamton University (SUNY) Location: Binghamton, New York Position Type: Full-time, Tenure-track Start Date: August 17, 2026 Application Deadline: Open until filled (posted October 16, 2025) The Department of Geography at Binghamton University (SUNY) invites applications for the position of tenure-track assistant professor in geography with research specialization in Geospatial Artificial Intelligence (GeoAI)/Spatial Data Science (and Geographic Information Science and Remote Sensing more broadly). Research: The candidate should have a methodological focus with collaborative research activity in an applied content area that integrates well with existing departmental strengths (e.g., urban, economic analytics, transportation, health, human-environment interactions). The candidate will be encouraged to initiate interdisciplinary research collaborations with colleagues in other groups on campus (e.g., School of Computing, the newly established Institute for AI and Society, Digital and Data Studies, Data Science Transdisciplinary Area of Excellence). Teaching: The candidate will teach a range of geospatial courses (aligning with their individual expertise) at undergraduate and graduate levels to support the departmental mission (e.g., Foundations of GIS, Programming in GIS, Advanced Remote Sensing) with the ability to create new courses in GeoAI/Spatial Data Science. The teaching load for the position is 2/2. Service: The candidate will supervise teaching assistants, serve on thesis committees, advise graduate-level research, and actively participate in department colloquia and other programming. Required Qualifications: - PhD in Geography, GIScience, Computer Science, or related field - Demonstrated record of conducting research in GeoAI/Spatial Data Science (e.g., thesis, dissertation, peer-reviewed publication, research assistant role) Skills: GeoAI, spatial data science, GIS, remote sensing, Python, programming, machine learning, spatial analysis