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Energy Data Lab - Summer Funded Training

Job in Atlanta, Fulton County, Georgia, 30383, USA
Listing for: Casualty Actuarial Society
Seasonal/Temporary, Apprenticeship/Internship position
Listed on 2026-03-12
Job specializations:
  • Science
    Data Scientist, Research Assistant/Associate
Salary/Wage Range or Industry Benchmark: 60000 - 80000 USD Yearly USD 60000.00 80000.00 YEAR
Job Description & How to Apply Below
Position: Open Energy Data Lab - Summer 2026 Funded Training Opportunity

Catalyst Cooperative has spent the last year or so developing educational materials for early career energy researchers, with generous support from the Sloan Foundation. We've created a set of interactive Python tutorials about core data and software engineering skills which are useful in energy research.

This summer we are organizing a 2-day energy data lab from August 11-12 at Georgia Tech. It will be a mix of tutorials, time to apply new skills to your own work, and networking opportunities. We hope this will provide you with some new skills and help you find people to collaborate with in your research career. We are intentionally holding it immediately before the Macro-Energy Systems (MES) Workshop from August 13-14 so that you can go to both!

The

lab is primarily for people who:
  • are graduate students or postdocs looking to use energy data in their research
  • feel nervous about coding, or are running into technical roadblocks in their work
  • feel underrepresented in energy research

We have space for up to 20 attendees.

Breakfast, lunch, and dinner will be provided to all participants. Additionally, Catalyst Cooperative has funding to cover the food, travel, and lodging expenses for up to 15 attendees as a travel grant. This travel grant includes 4 nights of hotel (the lab, plus attendance at the MES workshop) plus domestic transportation (reimbursed via Catalyst Cooperative).

We will accept self-funded participants as space allows. The tutorial section of the lab will pull from the following pool of topics:
  • working with non-CSV data types: JSON, XML, and Parquet
  • accessing remote data and APIs: the requests library
  • web scraping: the Beautiful Soup library
  • visual data exploration: pandas 201
  • making assumptions about data
  • moving from Jupyter notebooks into robust Python packages: the uv package and project manager
  • automated testing and debugging: pytest and pdb
We'll be accepting applications through  EDT and will email those accepted by the end of  EDT.

If you have any questions about the lab or the application, please contact us at data-labp.

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