- Develop computational models of group, and community behavior using approaches such as agent-based modeling, multi-agent systems, LLM-based agents, or related methods.
- Analyze large-scale behavioral datasets, which may include text, online community interactions, social networks, surveys, demographic information, or mobility trajectories.
- Apply statistical, causal inference, experimental, or counterfactual evaluation methods to study interventions and technology-driven changes in social systems.
- Investigate societal impacts of generative AI and other emerging technologies, including topics such as technology adoption, collective behavior, communication, coordination, and community outcomes.
- Collaborate with researchers to formulate and refine new research questions related to AI, human behavior, and society.
- Lead or contribute to manuscripts, experiments, analyses, figures, and other research outputs intended for publication.
Minimum Qualifications
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- Currently enrolled in a Ph.D. program in Computer Science, Information Science, Computational Social Science, Data Science, Industrial Engineering, Electrical Engineering, Physics, or another related quantitative field.
- Research experience in at least one of the following areas: computational social science; agent-based or multi-agent modeling; LLMs or NLP applied to social or behavioral questions; large-scale social, behavioral, or mobility data.
- Strong programming and quantitative research skills, including experience with machine learning, statistical modeling, or agent-based simulation.
- Experience analyzing empirical datasets and evaluating computational models against observed data.
- Demonstrated ability to conduct independent research and collaborate on interdisciplinary research projects.
Bonus Qualifications
- Experience with LLM-based agents, generative agents, agent-based simulation, or multi-agent systems for modeling human or social behavior.
- Research experience using large-scale social or behavioral datasets, such as online communities, social media, networks, behavioral traces, surveys, demographic data, or mobility trajectories.
- Experience validating computational or simulation models using real-world, observational, or experimental datasets, including model calibration and comparison with empirical observations.
- Familiarity with causal inference, experimental methods, intervention analysis, or counterfactual simulation.
- Experience studying collective behavior, social interaction, technology adoption and diffusion, community dynamics, or the societal impacts of AI.
- Familiarity with developing visualization tools, interactive interfaces, or reusable dashboards for communicating research methods and outcomes.
- Publication record in relevant conferences or journals, including venues such as ICWSM, IC2S2, ACL, EMNLP, CSCW, WebSci, AAMAS, AAAI, NeurIPS, KDD, CHI, or comparable venues in computational social science, AI, NLP, social computing, and related fields.
- Experience leading or contributing substantially to the preparation of research manuscripts for peer-reviewed publication.
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| Years of Work Experience Required |
0 |
| Desired Start Date |
1/18/2027 |
| Internship Duration |
3 Months |
| Position Keywords |
Computational Social Science, LLM Agents, Agent-Based Modeling, Multi-Agent Systems, Social Simulation, Network Science, NLP, Online Communities, Causal Inference, Counterfactual forecast |
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