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simulation, including O/D modeling, multimodal network modeling, agent-based or behavioral modeling Large-scale computing, cloud-native analytics workflows, and data engineering for mobility platforms AI/ML
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: • Transportation systems modeling and simulation, including O/D modeling, multimodal network modeling, agent-based or behavioral modeling • Large-scale computing, cloud-native analytics workflows, and data
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a key role in building and integrating of AI agents into gaming scenarios (e.g., gameplay, interactions, procedural content generation, dynamic narratives), and integrating a multimodal detection
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, biophysics, mathematics, or related field 8+ years of experience with agent-based models or physics-based models 5+ years of experience managing a scientific team of 5 or more people 2+ years of experience
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cues play a role analogous to conditioned stimuli: they are signals that, once learned, allow the agent to anticipate the consequences of its actions. Scientific Motivation: Learning-based navigation
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(Lua/Java), agent behavior modeling, event handling, and API-based integration with external AI systems. Experience with distributed systems, reinforcement learning, or simulation environments (e.g
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: Immunology, Animal Modeling, and Pathogen Unit. These units directly support resident DHVI/RBL faculty and are also available to support Duke faculty and their collaborators as fee-for-service shared
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emulators, or agent-based modeling. Knowledge of scenario development, resilience frameworks, and socio-environmental-technological systems. Experience with visualization tools (e.g., dashboards, GIS, spatio
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modeling builds on agent-based modeling but focuses on relations, not agents, and emphasizes the role of the researchers and their tools in constituting the model and the generated knowledge (Schlüter et al
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and develop complex, custom Artificial Intelligence models and applications. This role incorporates Machine Learning, Deep Learning, Computer Vision, Large Language Models, and Agentic AI technologies