2026 Internship Opportunities

Energy system optimization models are a critical analytical framework for evaluating the cost, emissions, and reliability implications of future energy system pathways under a range of technological and policy scenarios. These models rely on extensive, high-resolution datasets to represent complex energy systems that may encompass multiple sectors, including electricity, energy-intensive industries, and biomass. Input data typically include technology cost and performance parameters, retrofit and adoption decisions, demand projections, and infrastructure characteristics for the transport of energy and material commodities. To accurately capture system dynamics and produce meaningful results that can inform climate and energy policy, much of this data must retain high spatial and temporal resolution.

A major challenge in conducting such analyses lies in the preparation and dimensionality reduction of model input data. While highly detailed datasets are essential for robust and insightful results, their granularity can render optimization problems computationally intractable at regional or national scales. Consequently, careful aggregation is required to preserve critical system dynamics while maintaining model tractability.

This project will contribute to the development of a scientific software package for preparing input data for MacroEnergy.jl, a large-scale capacity expansion and resource optimization model for energy systems (see https://github.com/macroenergy/MacroEnergy.jl). The project will focus on creating user-friendly software to rapidly generate input datasets—a major bottleneck in producing timely modeling results, as researchers often need to evaluate multiple scenarios across different spatial and temporal resolutions. The work will involve the implementation and evaluation of temporal and spatial clustering algorithms, followed by testing their effects on model fidelity, computational performance, and policy-relevant outcomes. With sufficient progress, the project may also include developing an interactive data visualization and management dashboard to improve transparency, reproducibility, and usability in the data-generation process.

• Work with graduate students and post-docs on software design of the Macro data-processing pipeline and implementation of clustering algorithms
• Conduct comparative analyses of clustering techniques and assess their impacts on optimization results
  -Data gathering and curation
• Writing documentation of software
• Potentially develop a prototype graphical interface or dashboard to visualize spatial and temporal data and clustering outcomes

• Acquire foundational understanding of large-scale energy system optimization modeling and its role in quantitative policy analysis
• Gain experience in scientific software engineering, including modular code design and collaborative workflows
• Develop practical expertise in dimensionality-reduction and clustering methodologies applied to complex spatiotemporal datasets
• Understand how model architecture and data preprocessing influence policy insights and decarbonization strategy design

• Proficiency with Julia and/or Python required
• Experience with data pipelining, cleaning, and analysis required
• Experience with software development and documentation is strongly desired
• Experience with graphical user interface design and implementation is a plus
• Experience with energy systems engineering and/or optimization modeling is a plus

Computing work

• The ZERO Lab (Zero-carbon Energy systems Research and Optimization Laboratory) improves and applies optimization-based macro-energy systems models to evaluate low-carbon energy technologies, guide investment and research in innovative decarbonization solutions, and generate insights to improve energy and climate policy and planning. Prof. Jesse D. Jenkins is the Principal Investigator. See zero.lab.princeton.edu 
• 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. 
• 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.