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Field
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workflows, including adaptive, automated, or agent-based (agentic) workflows that integrate simulation, data analysis, and/or machine learning. Experience with computational workflows on large-scale HPC
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, or decision-making in autonomous driving or robotic systems Reinforcement Learning and Agentic Control: Hands-on experience with reinforcement learning, multi-agent systems, or planning-based agents
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sparse-regression based techniques to derive interpretable and computationally efficient differential equation models from computationally intensive multi-cellular agent based models (ABMs) of Epstein–Barr
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support research in: • Transportation systems modeling and simulation, including O/D modeling, multimodal network modeling, agent-based or behavioral modeling • Large-scale computing, cloud-native analytics
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(FONPs) with different surface functionalization by photosensitizers and/or targeting agents should be developed for X-rays, ultrasound activations. Synthesis of PFC nanodroplets containing O2 will lead
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knowledge representation • Agent-based and simulation modeling • AI/ML, foundation models, causal inference, and predictive analytics • Human factors, behavior science, and patient-centered design • Advanced
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, including the ability to clearly present technical concepts and research outcomes. Preferred Requirements • Experience with LLM/VLM/VLA fine tuning • Familiarity with autonomous driving or multi-agent
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robots and competency in the Robot Operating System, and/or Physics Engine Simulators are preferred. The terms of employment are very competitive and include housing and educational subsidies for children
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: Develop data-driven numerical models (opinion models, norm dynamics, multi-agent systems). Network Science: Study social and temporal interaction networks using network physics tools. Simulation: Conduct
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relationships between data and metadata. Collaborate on innovative solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and AI processes (e.g., model training