Mentor Areas
Project leaders are multi-method political science/international relations experts with background in security, human rights and law.
Description:
Undergraduate Research Assistant Opportunities
Border and Boundaries Project, Perry World House, University of Pennsylvania
The Border and Boundaries Project at the University of Pennsylvania, led by Professor Beth Simmons, invites applications for Undergraduate Research Assistants (RAs) to join our research team beginning in Summer 2025. The project examines how states use technological structures and systems to govern borders, their adoption and diffusion over time and across world regions, and their diverse implications for political authority, sovereignty, human rights, and human mobility. Research Assistants will work closely with Professor Simmons and postdoctoral fellows to support the project's ongoing empirical and computational tasks.
We are hiring for two distinct undergraduate RA positions. Each position focuses on research tasks and requires different technical skill sets. These are paid positions, with expected commitments of 10 hours of work per week.
Position 1: Research Assistant for Data Coding and Validation
(Summer 2025, with possible continuation in Fall 2025 and Summer 2026)
This role focuses on supporting the coding and validation of an original dataset on global border technologies. RAs in this position will be primarily responsible for hand-coding border infrastructure and technological systems and conducting data validation, consistency checks, and quality control. The work will involve reviewing government documents, policy reports, and media sources to record and verify information accurately.
This position suits students interested in political science, law, and related disciplines eager to gain hands-on experience in large-scale empirical data collection and contribute to a high-impact, policy-relevant research initiative.
Position 2: Research Assistant for AI & Natural Language Processing (NLP)
(Summer 2025, with possible continuation in Fall 2025 and Summer 2026)
This position involves supporting the application of computational methods and AI tools to classify textual data. RAs will assist with model training using hand-coded data, prompt engineering, and implementation of machine learning pipelines using Python, R, or Google Colab. Tasks will include comparing results and evaluating outputs from different large language models (LLMs).
This role provides an excellent opportunity for students from diverse backgrounds interested in data science in general and in applying artificial intelligence and computational tools in particular.
Both RA positions will offer mentorship and skill-building opportunities, including regular check-ins with project leads, optional workshops, and possible opportunities for future collaboration. RAs will receive compensation in line with Penn’s undergraduate research assistant pay structure. Positions may be remote or hybrid depending on the student’s location and project needs.
Students from all relevant academic backgrounds are welcome to apply. To express interest or request more information, please get in touch with the project team at simmons3@law.upenn.edu.
Preferred Qualifications
Position 1: Research Assistant for Data Coding and Validation, Key Skills and Expectations:
- Attention to detail and ability to follow complex guidelines/protocols
- Experience in using Excel (e.g., sorting, filtering, data entry) is required
- Some prior exposure to hand coding data is preferred
- Strong time management skills and ability to meet regular deadlines
- Ability to work independently while responding to periodic guidance and feedback
- Basic exposure to Python or R is a plus (not mandatory)
Position 2: Research Assistant for AI & Natural Language Processing (NLP), Key Skills and Expectations:
- Attention to detail and ability to follow complex guidelines/protocols
- Basic proficiency in Python, R, or/and Google Colab is required
- Experience in programming and data processing is preferred
- Familiarity with basic machine learning applications (e.g., classification, model evaluation) is preferred
- Interest in working with or learning about large language models (LLMs) and their applications in political science research
Details:
Preferred Student Year
Second-Year, Junior, Senior
Academic Term
Summer
I prefer to have students start during the above term(s).Volunteer
No
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.