2026 Internship Opportunities

Professor Ramaswami’s Urban Nexus Lab is leading several projects that use Machine Learning (ML) and Computer Vision (CV) to quantify global infrastructure, including buildings and roads, as well as environmental features such as trees and green cover. The internship will focus on such topics in India, with the possibility of expanding to other countries depending on the intern’s experience and progress. Interns will work with diverse datasets, including remote sensing and localized data sources (e.g., building permits). While much of the groundwork has been laid by the lab’s previous work, the group encourages creativity and new ideas to connect data science with sustainable urban systems research.

Sample Project: One example involves developing scalable methods to estimate urban building material stocks by combining street-level and satellite imagery with tabular data. This approach bridges the gap between detailed local data and scalable global models, enabling globally consistent yet locally relevant insights for urban sustainability and material flow analysis.

The intern will have four key responsibilities:

• Assisting with data integration from diverse sources.
• Developing and improving machine learning pipelines using R or Python for infrastructure and environmental quantification.
• Training and validating datasets from cities across India.
• Supporting Urban Nexus Lab research teams with data science inputs.

Students will:

• Understand how data science intersects with sustainable urban systems and informs city policy.
• Learn to identify appropriate data and methods for real-world urban research.
• Work with multi-source datasets (satellite, GIS, and survey data).
• Develop a conceptual understanding of spatial datasets.
• Strengthen technical skills in machine learning and computer vision.
• Gain exposure to urban issues globally and how data science is transforming their study.

Students with a Computer Science or Engineering background and an interest in urban systems science are encouraged to apply.

• Experience with machine learning and computer vision (coursework or projects).
• Proficiency in one or more programming languages (R, Python, MATLAB, etc.).
• Basic familiarity with data analysis, data science, or remote sensing.
• Experience with GIS and remote sensing tools (e.g., ArcGIS, QGIS, GRASS GIS, ENVI, or Google Earth Engine).

• A basic orientation to the lab’s work and the projects the students will be involved in will be provided. This will include reviewing relevant papers and presentations, as well as project-specific data training. All of these activities will take place once the internship begins.
• Students are responsible for securing accommodations for the duration of their internship. On-campus summer housing is typically available the first week of June until the first week of August (details to become available in Spring 2026). If the internship falls outside of on-campus housing availability, students will need to secure alternate housing.