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, including large-scale optimization and meta-heuristics; economic paradigms (game theory, mechanism design, electronic markets); agent modeling and simulation; agent planning, scheduling and decision
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example, development of AI agents for integration and maintenance of computational codes written in different languages (in particular MATLAB, Python and Fortran) development of AI agent-based
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animal models, and/or whole farm models with explicit modeling of soil processes and plant growth. Familiarity with system dynamics and agent-based modeling approaches. Demonstrated ability to
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and behavioral responses to autonomous mobility services. Develop and estimate discrete choice models for transportation mode choice decisions. Implement and apply agent-based transport simulations
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environments. The project will explore how learning-based and agentic AI systems (e.g., agents built on large language or foundation models) can be combined with physics-informed process models, digital
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Earth Sciences, including physics-informed machine learning, climate and environmental modeling, physics and astrophysics applications, and digital twinning for material design and manufacturing. DS/AI
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interested in: LLM -driven systems for aligning real-world health data to standards like OMOP CDM , FHIR , and UMLS Agent-based workflows that explain, refine, and adapt semantic mappings over time Hybrid
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interested in: LLM -driven systems for aligning real-world health data to standards like OMOP CDM , FHIR , and UMLS Agent-based workflows that explain, refine, and adapt semantic mappings over time Hybrid
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approaches such as system dynamics, causal modeling, scenario analysis, participatory modeling, agent based modeling, or related frameworks. ● Excellent written and oral communication skills
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Multiple Research-Intensive Associate/Full Professor Tenure System Positions & an 1855 Professorship
, implementation science, geospatial analysis, biostatistics and research design, analysis of interventions (e.g., difference-in-differences), AI analytics, agent-based modeling, Bayesian modeling, causal