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Field
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The Computer Vision Group is looking for an aspiring PhD to investigate multi-agentic AI, LLMs, and VLMs applied to agricultural sciences. Currently, established AI models often fail to generalize
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the field. Perform quantitative analysis and agent-based modeling of behavior. Report, discuss, and present data to the team. The position is for 36 months. Laboratory work using virtual reality (VR
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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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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
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for Artificial Intelligence) project, where newly admitted PhD students will research and develop large language models and agentic interfaces for multilingual knowledge management, using high-quality
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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
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on complex CO₂ transformations—with applied research aimed at developing more sustainable and circular fermentation processes. We will seek to adapt boron- and silane-based reducing agents, as
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project “Responsible AI for the Swiss Judiciary” at ETH Zurich. The project develops and evaluates AI-based prototypes to support judicial decision-making, with a focus on real-world applicability. It
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. You will develop dynamic models and apply them, for example, to analyze sociotechnological networks and to model interactions between humans and AI agents (such as LLM-based chatbots and autonomous
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://cavecore.eu/ Your Research Project (DC4) You will work at the intersection of machine learning, control theory, and autonomous multi-agent systems to develop hybrid learning-based control strategies