AI Research Scientist: Computational Social Systems ...

About the role

Honda Research Institute USA (HRI-US) is seeking an AI Research Scientist to develop machine learning, simulation, and computational modeling methods for understanding and forecasting complex social systems shaped by AI-enabled technologies.

Responsibilities

• Develop AI and computational modeling methods for simulating social, organizational, community, and socio-technical systems.
• Build multi-agent, network-based, causal, probabilistic, system-dynamics, complex-systems, generative agent, or hybrid simulation models.
• Design counterfactual and scenario-based simulations to compare alternative AI deployment strategies, policies, interventions, and system designs.
• Model social adaptation mechanisms such as trust, adoption, reliance, coordination, norms, learning, information diffusion, equity, resilience, and emergent collective outcomes.
• Apply and advance AI methods, including foundation models, LLM-based agents, multi-agent AI systems, generative simulation, graph learning, causal modeling, and uncertainty-aware inference.
• Integrate empirical datasets into modeling pipelines, including behavioral logs, mobility data, survey data, experimental data, public administrative data, demographic data, and human-AI interaction data.
• Evaluate model performance, uncertainty, sensitivity, assumptions, limitations, and implications using appropriate validation methods.
• Collaborate with experimental and interdisciplinary researchers to connect model predictions with human-subject studies, field data, and system-level evidence.
• Translate research results into publications, prototypes, technical reports, scenario-analysis tools, and internal decision-support systems.
• Contribute to HRI’s long-term research strategy on responsible, human-centered, and socially aware AI systems.

Requirements

• Ph.D. in computer science, artificial intelligence, machine learning, computational social science, complex systems, systems engineering, statistics, economics, cognitive science, public policy with strong computational training, or a related quantitative field.
• Strong research background in AI/ML, computational modeling, simulation, or data-driven modeling of social, behavioral, organizational, or socio-technical systems.
• Demonstrated ability to formulate complex real-world systems as computational models and evaluate those models using empirical data.
• Expertise in at least one of the following areas: multi-agent systems, agent-based modeling, generative-agent simulation, causal inference, counterfactual modeling, graph or network modeling, probabilistic or Bayesian modeling, or simulation-based forecasting.
• Experience applying AI/ML methods to modeling, prediction, simulation, decision support, or human/social behavior analysis.
• Strong programming skills in Python or a similar scientific computing language, with experience using AI/ML, simulation, statistical modeling, or data-science frameworks.
• Ability to communicate model assumptions, uncertainty, limitations, and implications to technical and interdisciplinary audiences.
• Demonstrated research output through peer-reviewed publications, prototypes, open-source tools, technical reports, patents, or applied research projects.

Qualifications

• Publication record in venues such as NeurIPS, ICML, ICLR, AAAI, AAMAS, CHI, CSCW, KDD, ICWSM, ACL, EMNLP, or comparable conferences and journals.
• Experience with LLM-based agents, generative agents, multi-agent AI systems, AI simulations of human behavior, or foundation models for social system modeling.
• Experience with large-scale computational modeling, counterfactual simulation, policy simulation, scenario-analysis tools, digital twins, or decision-support dashboards.
• Experience applying computational models to domains such as mobility, emergency response, education, public health, urban systems, workplace collaboration, community planning, or responsible AI deployment.
• Familiarity with behavioral science, decision science, social science theory, human factors, human-AI interaction, organizational modeling, or policy modeling.
• Experience working with real-world datasets, field data, experimental data, or human-subject research data.
• Ability to connect fundamental AI research to practical tools that support long-term technology strategy, responsible deployment, and measurable societal impact.