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market using data available on online recruiting platforms deploying state-of-art approaches in Natural Language Processing, SemanticWeb and Agent-based Modeling. For this purpose, an extensive
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. Advanced machine learning, reinforcement learning, and agent-based optimization techniques will be developed to reduce voltage deviations, cut active power curtailment, and improve system adaptability under
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deploying an AI project. This includes initial problem specification, data gathering and analysis, model creation, and implementation. Applicants should be willing to learn and use agentic AI to build and
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soon as possible. The successful candidates will be appointed at the University of Tübingen. The positions provide generous, permanent core funding (e.g. for PhD students or postdocs) and substantially
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. Assisting in the research and implementation of AI agent-based approaches for automated ontology alignment and semantic integration across distributed systems. Participating in the design and validation
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conversational AI, dialogue systems, human–agent interaction, adaptive systems, user modelling, multimodal interaction, or a related area; Experience with empirical research, preferably including user studies
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microbial inhibition models Model-based data processing Excellence in both written and spoken English You will be working in the research group “Microbial Conversion Technologies” as part of the Dept
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discipline. A Master’s degree or PhD is preferred. Experience: Minimum of five years of related experience. Must have previous experience managing projects. Experience must include a minimum of one-year in a lead
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through proprioception. We develop widely-used open-source tools (e.g., DeepLabCut), train biomechanically realistic embodied agents (e.g., MuscleMimic, Kinesis, Arnold), and build AI-based models
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integrates Agent-Based Models, hydrological and flood models. GEB can simulate the dynamics between multiple hazard risk, how these risk impact society, vulnerability of different sectors, and how society can