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Field
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and companies interested in testing their own models and/or choosing the right combination of agents for their needs Participating to research papers and materials presenting and promoting the results
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Scene Synthesis: Re-design diffusion and NeRF-style models so multiple agents jointly reconstruct a scene. Cooperative Scene Understanding: Re-design semantic segmentation and object-detection models in a
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(e.g., Reinforcement Learning, Agent Based Modelling) to join our team full-time as part of a large international collaboration of European researchers (incl. Tobias Dienlin, Veronica Kalmus, Adrian
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networks of LLM-based agents to solve complex problems has shown great potential, for example in DeepMind’s Co-Scientist System. Against this background, the goal of this project is to reframe the field
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of the PhD topic (subproject A7- Reinforcement learning for mode choice decisions): This PhD project will develop and implement a Deep Reinforcement Learning (DRL) model for dynamic mode choice within
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Laboratory seeks a postdoctoral appointee to join a multidisciplinary team developing complex systems models, including agent-based models, and new algorithms and tools for machine learning and optimization
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. Research Focus The rise of powerful Large Language Models (LLMs) and autonomous AI agents is set to fundamentally change the practice of software engineering. As AI evolves from simple co-pilots to agents
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Language Model (LLM) Strong biostatistics knowledge including survival analysis and causal inference Experience with reinforcement learning, agentic AI systems and autonomous decision-making frameworks Data
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. Desirable criteria Track record of interdisciplinary collaboration. Familiarity with cognitive architectures or agent-based modelling frameworks. Ability or potential to contribute to the development
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in AI: Generative Diffusion & 3D/4D Scene Synthesis: Re-design diffusion and NeRF-style models so multiple agents jointly reconstruct a scene. Semantic-Aware Compression & Network Information Theory