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argumentative agentic AI approach for chemical development settings based on reinforcement learning but able to shape rewards with the help of ontological knowledge as well as expert knowledge from humans and
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identification of optimization levers while ensuring compliance with safety requirements. The work will be based on process and decision modeling standards (notably BPMN and DMN), enhanced by agent-based AI
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the creation of realistic simulated environments based on environmental data. - Defining evaluation protocols and performance metrics (safety, energy, mission efficiency). - Contributing to scientific validation
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simulations, so that they maintain their validity. The process consists in evaluating a sequence of overlapping mental simulations to construct the future course of actions that the agent will take, based
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of cell factory engineering. Develop software tools that enable programmatic interaction between large language model agents and metabolic models, enabling automated simulation, interpretation, and
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Your Job: In this master thesis, a multi-agent-based local energy market simulation ASSUME shall be extended to account for district grid constraints. The objective is to investigate how local grid
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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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-preserving techniques, and robust data curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model experimentation and training. Science of
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, and can analogous mechanisms be engineered into multi-agent AI systems? You would answer this question by building and testing computational models, developing multi-agent simulations where agents
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curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model experimentation and training. Science of Deep Learning: Exploring mechanistic