Assistant Professor (Spatial Statistics and GeoAI)

University of Hawaii; Honolulu (HI); On-site; USD 105,000 - 135,000; Full time

Job summary

University of Hawai'i at Manoa invites applications for an Assistant Professor (F-3) in Spatial Statistics and Geospatial AI. This 9-month, tenure-track position begins August 1, 2027.

We seek a scholar advancing spatial statistics, ML, and AI for geographic processes and environmental and social challenges. The appointee will teach, supervise graduate students, develop an active research program, seek extramural funding, and engage in service to department and community.

Qualifications

• Ph.D. required (ABD considered) in Geography, GIS, Statistics, or related field; PhD conferred before hire.
• Expertise in spatial statistics or spatial modeling and data science, ML or AI applied to geospatial data.
• Ability to teach undergraduate and graduate courses; mentor students.
• Proficiency in Python and/or R.

Responsibilities

• Teach undergraduate and graduate courses in Spatial Statistics and related topics.
• Supervise and mentor MA and PhD students; support undergraduate research.
• Develop an active research program and publish scholarly work.
• Seek extramural funding to support research and training.
• Provide service to Department, College, University, profession, and community.
• Other duties as assigned by the Department Chair.

Skills

Spatial statistics

Spatial statistics modeling

Data science

Machine learning

Artificial intelligence

Python

R

Teaching

Mentoring

Grant writing

Education

PhD in Geography, GIS, Statistics or related field

ABD considered with PhD by hire date

Tools

Python

R

GIS

Job description

Assistant Professor (Spatial Statistics and GeoAI)
University of Hawai'i at Manoa in Honolulu, HI

Type: Full-Time

Salary: Commensurate with qualifications and experience

Application Due: Open Until Filled

Category: Geography

The Department of Geography and Environment, College of Social Sciences, University of Hawai'i at Manoa (UHM, Honolulu, Hawai'i) invites applications for an Assistant Professor (F-3) in Spatial Statistics and Geospatial Artificial Intelligence (GeoAI). This is a 9-month, full-time, permanent, tenure-track faculty appointment to begin August 1, 2027, subject to position clearance and availability of funds.

We seek a scholar whose research advances spatial statistics, statistical learning, machine learning, or artificial intelligence (AI) for understanding geographic processes and social and environmental challenges. Candidates should demonstrate a strong foundation in spatial analysis and the ability to connect methodological development with substantive applications. Relevant application areas include, but are not limited to, environmental change, human-environment interactions, climate variability, natural hazards, land-use and land-cover change, population dynamics, spatial inequality, health geography, and agricultural and ecological systems. This position will strengthen the Department's expertise in spatial statistics, complementing its existing strengths in geographic information science (GIS) and remote sensing. Together, these three areas support an integrated approach to geospatial research and education. More information about the Department of Geography and Environment is available at https://geography.manoa.hawaii.edu/.

This position will participate and contribute to the College of Social Sciences' new SODA (Social Science Data Analytics) initiative through the development and teaching of two shared undergraduate courses open to students across the College (covering topics in data science, statistical learning, machine learning, and artificial intelligence for the social sciences), alongside advanced graduate instruction in spatial statistics.

UHM is an R1 (Very High Research Activity, Carnegie Classification) institution located on a beautiful arboretum campus. UHM is a land-grant, sea-grant, space-grant, and sun-grant university with the core mission of generating, preserving, and disseminating knowledge for the greater good of the people of the state of Hawai'i and the world beyond.

Duties and Responsibilities

• Teach undergraduate and graduate courses such as Spatial Statistics, Spatial Statistical Modeling, and the College of Social Sciences shared quantitative-methods sequence (covering topics in data science, statistical learning, machine learning, and artificial intelligence for the social sciences), developed as part of the College's new SODA initiative;
• Supervise and mentor MA and PhD students and support undergraduate research;
• Develop an active research program and publish scholarly, peer-reviewed research in areas of expertise;
• Seek extramural funding to support research and student training;
• Provide service to the Department, College, University, profession, and community;
• Other duties as assigned by the Department Chair.

Minimum Qualifications

• Ph. D. in Geography, Geographic Information Science, Statistics, or a closely related field from a college or university of recognized standing, or foreign equivalent. ABD applicants will be considered, but all degree requirements must be completed and the Ph. D. conferred before the date of hire;
• Demonstrated expertise in spatial statistics or spatial statistical modeling and in data science, machine learning, or artificial intelligence applied to geospatial data;
• Ability to teach undergraduate and graduate courses in areas of expertise and to teach statistical, machine learning, and AI methods to social science students;
• Proficiency in scientific programming using languages such as Python and/or R;
• Ability to mentor and advise students in the applicant's area of research;
• A record of research achievement commensurate with time in the field;
• Demonstrated ability to maintain professionalism in meeting and conferring with others.

Desirable Qualifications

• Research that connects spatial statistics and GeoAI with substantive social or environmental questions and complements the Department's strengths in GIS and remote sensing.
• Experience teaching quantitative or computational methods using applied exercises and reproducible workflows.
• Demonstrated ability to achieve interdisciplinary collaboration and secure extramural funding.