PhD Position in Artificial Intelligence and Urban Science (Fall 2025)

The Urban Information Lab at The University of Texas at Austin is seeking two motivated students to pursue a PhD in Community and Regional Planning with a focus in Artificial Intelligence and Urban Science starting in Fall 2025. The successful candidates will work under the supervision of Dr. Junfeng Jiao to conduct empirical analysis and optimization research at the forefront of urban technology.

The positions offer generous research or teaching assistantships, which include tuition coverage and a stipend. Information about the PhD program in Community and Regional Planning is available here. Successful applicants will be fully funded through one of the following two units:

●    NSF Ethical AI Fellowship

●    Urban Information Lab at UT Austin

Qualifications: The ideal candidate must hold one master degree in one of the following disciplines: Urban PLanning, Computer Science, Information Science, Mathematics, Economics, Civil Engineering, GIS, or closely related fields. A good knowledge of quantitative techniques, along with experience using Python, R, GIS, or other statistical and data analysis tools, or a desire to improve these skills, will be viewed positively. Experience with academic publishing in peer-reviewed journals is also considered an asset. Clear communication and teamwork experience are encouraged.

Preferred Qualifications: Relevant expertise or at least one peer-reviewed publication

Research Areas and Projects

This unique specialization combines the study of cutting-edge AI technologies with urban planning, preparing students to address complex urban challenges through data-driven approaches and ethical AI applications. Students with experience in optimization research, geospatial modeling, AI modeling (including Generative AI), and the application of AI agents are highly encouraged to apply for this position. Research opportunities include AI in transportation, energy and utilities, digital twin, housing, disaster resilience, and more. Specific research avenues include, but not limited to:

●    Transportation and Energy: Evaluating the energy impacts of electric vehicles (EVs) and autonomous vehicles (AVs), including mass adoption of RoboTaxis; optimizing AV robot delivery systems; and leveraging AI applications to optimize public transit operations. Evaluation of Vehicle-to-Grid (V2G), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X) technology.

●    AI Applications and Urban Technology: Computer vision techniques and Landsat analysis; application of LiDAR and radar to investigate urban form; and the use of LLMs to develop a sustainable AI index for global cities.

●    Digital Twin: Leveraging 3D layers and their potential; applying machine and deep learning to improve accuracy in quantitative modeling; and creating clear visualizations for city modeling.

●    Housing: Modeling AI agents to assess housing prices and market values; creating chatbots using natural language processing techniques or Transformers for homeowners; and applying deep learning techniques predicting housing market trends and price fluctuations.

●    Disaster Resilience: Scenario testing of natural hazard events using digital twin modeling; smart water and floodplain management with AI; leveraging GeoAI to quantify landscape assessments; design green infrastructure using AI.

Admission Process: To apply, please follow the general admission process for the doctoral program in Community and Regional Planning at The University of Texas at Austin.

For more information and contact, please visit the Urban Information Lab website: and reach out to the lab manager (Dr. Kijin Seong: kijin.seong@austin.utexas.edu). Sending a CV and Cover Letter is encouraged.

Acquire the Admission Guide