Urban Heat Data Science Intern

Tags: internship Environment
  • Added Date: Thursday, 06 March 2025
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About the Program:

The Ross Center for Sustainable Cities (https://www.wri.org/cities) builds on WRIโ€™s global and local experience in urban planning and mobility and uses proven solutions and action-oriented tools to make the fast-growing urban environment more resilient to new challenges. Our Data for Cool Cities (https://www.wri.org/initiatives/data-cool-cities) initiative aims to combat extreme heat in urban areas. Not only is heat magnified in cities due to the urban heat island effect, cities also are home to most of the worldโ€™s population, and vulnerability to heat arises from complex interactions between social, physical, and climatic systems. The Global Cities Data & Tools team is mapping the potential for heat-resilient infrastructure like street trees and cool roofs and modelling the impacts of implementation on the variation of thermal comfort within neighborhoods. By providing innovative data, enabling insights, and promoting sustainable infrastructure choices, this project seeks to improve urban livability, encourage effective governance, and enhance equity.

This is a hybrid position which requires 8 days per month in the office. You can be based in our office in Washington DC Office. Existing work authorization is required at the time of application submission as WRI is unable to sponsor any visa work sponsorship for this position. To be eligible for this position, you must be a resident of DC, Maryland or Virginia at the start of employment.

What you will do:

You will be responsible for integrating equity into your work. In this role you will work to support our urban heat modeling program by conducting independent research on how the results of a local-scale heat model vary across diverse climates and urban environments. The primary tasks include validating input and output data; running the urban heat model and documenting the modeling workflow; and summarizing the outputs and extracting insights.

In particular, you will:

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