Faculty Position in Spatial Information (GeoAI & Spatial Data Science) at National Chengchi University
The Department of Land Economics at National Chengchi University (NCCU), Taipei, Taiwan, invites applications for one full-time, open-rank faculty position in Spatial Information, beginning 1 August 2027.
We welcome scholars with expertise in one or more of the following areas: GeoAI, spatial data science, and dynamic analysis of urban and environmental space and its impacts. The successful candidate will demonstrate strong potential for interdisciplinary research and teaching related to spatial information, artificial intelligence, urban and environmental analysis, land governance, climate adaptation, and intelligent spatial governance.
Applicants must hold a PhD and will be appointed at the rank of Assistant Professor or above. The appointee will teach undergraduate and graduate courses in relevant areas; at least one undergraduate course and the graduate course must be taught in English.
How to apply
Please review the complete official recruitment announcement, including eligibility criteria, required application materials, and submission instructions:
https://www.nccu.edu.tw/p/406-1000-23687,r40.php?Lang=en
Application Deadline: 2 November 2026.
Contact: lgchen@nccu.edu.tw
The official NCCU announcement is authoritative. Applicants should consult it for any revisions or deadline extensions.
Full PhD scholarship available at Adelaide University, South Australia:
SRTSR0347 Constellation-Scale Onboard AI for Early Wildfire Detection.
Apply now (due 30/9):
https://app.smartsheet.au/b/form/019eedeef91978fdbc2b67718479eac6?project_id=SRTSR0347
Wildfires cause severe loss of life, property and ecosystems, making rapid detection vital. Small satellites can now use onboard AI to detect fires and transmit alerts rather than imagery, as demonstrated by our team on different SmallSat missions.
This PhD will address two key limitations: limited labelled training data and the isolated analysis of individual satellite passes. It will investigate Earth-observation foundation models to improve fire detection from limited training data and develop lightweight models for onboard deployment. It will also explore agentic AI and constellation-scale approaches to combine repeated observations across Australian-led satellite constellations, helping confirm fire progression, reduce false alarms and prioritise urgent alerts. The research will advance Adelaide University’s SmartSat CRC-funded work towards operational early wildfire detection.
See also https://adelaide.edu.au/study/how-to-apply/research/
Contact: Stefan Peters, stefan.peters@adelaide.edu.au