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Field
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interests are interdisciplinary modeling (ideally using economic production theory, more specifically Data Envelopment Analysis, system dynamics modeling/agent-based modeling, and/or Artificial Intelligence
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Perturb-seq, with our integrative analyses often employing computational approaches such as agent-based modeling to deconstruct gene-regulatory networks and predict system behaviors. As a postdoctoral
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About the Opportunity The successful candidate will contribute to an ambitious project developing perceptual AI agents that assist humans in daily activities through behavioral understanding and
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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) department at Telecom Paris. Reinforcement learning (RL) has emerged as a useful paradigm for training agents to perform complex tasks. Model-based RL (MBRL), in particular, promises greater sample efficiency
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STÅSTEDER project anchored at DTU in collaboration with the municipality of Aarhus. Your expertise in LLMs and synthetic agents will help the MOVEO project to develop perspectival models based on ethnographic
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Experience with methods such as Stochastic Actor-Oriented Models (SAOM) or agent-based modeling (ABM) is an advantage but not required What We Offer A supportive, lively research environment with international