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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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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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supervision of PI Calina Copos and will engage in collaborative work with cell and developmental biologists. Expertise in agent-based models, continuum PDE descriptions, dynamical systems, and/or ML-based
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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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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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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
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Limitation:Temporary (3 years) Contract:TV-L Your Tasks Develop and apply mathematical models (including microsimulation, agent-based, state-transition models) to simulate tools and interventions used before, during and
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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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, PyTorch, JAX etc. Experience with modern AI concepts such as large language models (LLMs), vision-language models (VLMs), model context protocol (MCPs), and the development of agentic AI tools. Skill in
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+ 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 working with a product management team to