Urban Heat Data Science Intern

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Description

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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:

• Compare microclimatic conditions for extreme heat in diverse cities
• Investigate differences in heat exposure and impacts of mitigation strategies
• Augment model documentation with insights about process & performance in diverse geographic contexts
• At the end of your internship, you will have the opportunity to present your findings to our Cities Data Community of Practice. You will be supported by the Global Cities Data & Tools team and will report to Drs. Elizabeth Wesley and Ruth Engel.

Internship Learning Outcomes:

• Validating geospatial data and products.
• Implementing the urban heat model and documenting a reproducible workflow.
• Designing research to extract key insights on heat exposure in cities and model performance.
• Reporting results in written, tabular, and visual form.

What you will need:

• Currently pursuing or recently completed a bachelor’s or master’s degree.
• Ability to read and write Python and basic familiarity with R. Experience with Git preferred.
• Proficiency with geographic data science and geospatial analysis including both raster and vector data formats and spatial statistics. Experience with QGIS preferred.
• Experience using physical or statistical models to produce spatio-temporal data preferred but not required.
• Strong written communication skills.
• Interest in applied research to address the challenge of extreme heat in cities.

Source: https://jobs.jobvite.com/wri/job/oziyvfwE

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