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Xiaojiang Li

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PhD Position in Urban Microclimate Modeling, Geospatial AI, and Urban Planning University of Pennsylvania in United States

Degree Level

PhD

Field of study

Computer Science

Funding

The position is fully funded by NSF and includes a full funding package covering tuition, stipend, and health insurance. Additional benefits include access to advanced computing resources, urban data partnerships, and strong mentoring in research, teaching, and professional development.

Deadline

Feb 25, 2026

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Country

United States

University

University of Pennsylvania

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Keywords

Computer Science
Environmental Science
Deep Learning
Remote Sensing
Geography
Urban Planning
Civil Engineering
Salud Pública
Climate Resilience
Urban Climate
Machine learning

About this position

The Urban Spatial Informatics Lab (USIL) at the University of Pennsylvania is offering a fully NSF-funded PhD position starting Fall 2026. This opportunity is ideal for students interested in urban microclimate modeling, geospatial artificial intelligence (AI), and spatial data science. The research will focus on urban microclimate and heat exposure modeling, leveraging advanced geospatial AI and deep learning techniques such as CNNs, U-Net, ViT, and foundation models. The work will also involve urban spatial analytics and planning applications, with a strong emphasis on equity, resilience, and public health.

Ongoing projects in the lab include high-resolution urban heat modeling, AI-accelerated microclimate simulation, scenario-based climate adaptation planning, and the integration of remote sensing data (LiDAR, aerial imagery, satellite data) with urban planning and health datasets. The research spans case studies in both U.S. and global cities, providing a broad and impactful context for the work.

Applicants should have a background in urban planning, environmental science, architecture, geography, civil or environmental engineering, computer science, or related fields. Essential skills include a solid foundation in urban climate, environment, or spatial analysis, programming experience in Python, and familiarity with GIS and geospatial data. Experience or interest in deep learning or machine learning (e.g., PyTorch, TensorFlow) is expected. Prior experience with microclimate models (such as OpenFOAM, WRF, ENVI-met, Rhino plugins), computer vision, GeoAI, or large-scale spatial datasets is highly desirable but not required.

The position offers a comprehensive funding package covering tuition, stipend, and health insurance. Students will benefit from a collaborative, interdisciplinary research environment, access to Penn’s advanced computing resources, and engagement with policy-relevant and community-facing urban climate projects. Strong mentoring in research, teaching, and professional development is also provided.

To apply, interested candidates should fill out the online form by February 25th, 2026. Preferred candidates will be invited to submit a formal application through the University of Pennsylvania's application system. Applications are reviewed on a rolling basis, and those who have already applied before December do not need to reapply but may submit additional materials if available.

For more information about the lab and research, visit the Urban Spatial Informatics Lab website or contact Assistant Professor Xiaojiang Li at the University of Pennsylvania.

Funding details

The position is fully funded by NSF and includes a full funding package covering tuition, stipend, and health insurance. Additional benefits include access to advanced computing resources, urban data partnerships, and strong mentoring in research, teaching, and professional development.

What's required

Applicants should have a background in urban planning, environmental science, architecture, geography, civil or environmental engineering, computer science, or related fields. Required skills include a solid foundation in urban climate, environment, or spatial analysis, programming experience in Python, and familiarity with GIS and geospatial data. Experience or interest in deep learning or machine learning (e.g., PyTorch, TensorFlow) is expected. Prior experience with microclimate models, computer vision, GeoAI, or large-scale spatial datasets is highly desirable but not required.

How to apply

Fill out the provided online form by February 25th, 2026. Preferred candidates will be invited to submit a formal application through the University of Pennsylvania's application system. Applications are reviewed on a rolling basis.

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