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modeling Experience with machine learning, hydrologic modeling, or energy systems modeling (e.g., ParFlow, WEPPCloud, agent-based models) Proficiency in scientific programming languages (e.g., Python
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integrate physics-based simulations with experimental data and enable artificial intelligence (AI)-agent-assisted scientific workflows. This position will contribute to multidisciplinary research connecting
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clinically characterized samples, and use the results to test and refine the lab’s theory of agency. The work will generate first-author papers and an agent-based research line the candidate can carry
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the integration of both ecological and social data using platforms such as Marxan or prioritizr Experience with agent-based ecological modelling approaches (e.g., using R, NetLogo, or similar) Working
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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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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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-based approaches such as system dynamics, causal modeling, scenario analysis, participatory modeling, agent based modeling, or related frameworks. ● Excellent written and oral communication
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empirical findings into an agent-based model and cost-effectiveness analysis and to translate study findings into a decision-support toolkit for public health agencies and policymakers. The fellow is
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modelling using R. Proficiency in microsimulation and agent-based modeling. Excellent written and oral communication in English. Excellent personal time management, project management and