2026 Internship Opportunities

Industrial decarbonization represents a new frontier in energy-systems research, posing unique challenges distinct from the electricity and transportation sectors. Modeling the industrial decarbonization pathway is essential but challenging, as the term “industry” includes thousands of distinct subsectors, each with complex material flows, and diverse process technologies.

A critical barrier to advancing industrial decarbonization modeling is data availability. Many of the datasets required to represent industrial processes (including investment and operational costs, facility or process unit age, energy balances, and production data) are proprietary or inconsistently reported, and may vary widely between plants. Without this information, it is impossible to realistically model low-carbon retrofit decisions, since the economic and technical feasibility of retrofitting depends on both the cost and performance characteristics of existing equipment and whether each unit is nearing the end of its operational lifetime. Accurately representing these factors is essential to identify which facilities can be decarbonized cost-effectively and to avoid unrealistic assumptions about early retirements or stranded assets.

This project will contribute to efforts to close these data gaps and improve industrial representation in the Macro model, a large-scale multi-energy system capacity expansion and resource optimization tool developed by the ZERO Lab and other collaborators. The work will involve developing and testing novel methods to identify, extract, and infer plant-level data, potentially using a combination of web-scraping, database integration, and large-language-model-based information retrieval. This work will also require the intern to gain familiarity with industrial process technologies, data-engineering workflows, and the fundamentals of energy-systems modeling, in order to use estimation techniques in cases in which data cannot be found.

This project will be a good starting point to get familiar with the Macro model and continue on with industrial decarbonization research.

• Work with graduate students and post-docs to understand data needs
• Research industrial process technologies to understand energy use, retrofitting options, and make informed estimates where data are unavailable
• Apply technical methods such as web-scraping, database integration, and large-language-model tools to identify and fill missing industrial data
• Develop a database of industrial facilities and process units for integration into energy-system models

• Acquire foundational understanding of large-scale energy system optimization modeling and its role in quantitative policy analysis
• Become involved in industrial decarbonization research and learn how research combines engineering knowledge, data analysis, and modeling
• Develop technical skills in data collection and database management
• Develop an understanding of how key industrial sectors can be decarbonized through various technology options (electrification, carbon capture, fuel switching, etc.)

Required Technical Skills:

• Data analysis and programming: Proficiency in Python (preferred) or R for data processing and analysis
• Data management: Experience with tabular data handling using tools such as pandas, NumPy, or SQL
• Basic web research / data collection: Ability to gather, clean, and validate information from multiple online and public sources
• Strong analytical and quantitative skills: Comfort working with data to identify trends, inconsistencies, and relationships
• Energy systems or industrial processes: Prior coursework or interest in energy systems, industrial engineering, or sustainability

Desired / Preferred Technical Skills:

• Web scraping and automation: Experience with tools such as BeautifulSoup, Selenium, or Requests to collect structured data from websites
• Database integration: Familiarity with relational databases (e.g., PostgreSQL, SQLite) and schema design
• Natural language processing / LLM tools: Exposure to OpenAI API, LangChain, or similar frameworks for text-based data extraction
• Data visualization: Familiarity with Matplotlib, Plotly, or Tableau for summarizing and communicating data findings
• Version control and collaboration: Basic use of Git and GitHub for code and data management

• 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.
• During the first week of the internship, a comprehensive orientation will be provided for ZERO Lab research objectives, our modeling tools (e.g. MacroEnergy.jl), prior experience developing data pipelining tools (PowerGenome), current data sets and sources, and project objectives.