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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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quantitative and qualitative research to develop empirically supported game-theoretic and agent-based models Apply econometric and model simulation methods to analyze governance mechanisms and to study the
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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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, 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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tools developed in the last decade, and compare the networks and task dynamics for the different conditions [11]. We will moreover consider various agent-based models, developed in statistical physics
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several major types of mobility modeling, with the aim of improving their respective efficiency and usage: four-step models, multi-agent systems and a mobility model developed in the PhD thesis of Louisette
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solutions for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2026/phd-student-in-applied-ml-and-… Requirements Research FieldComputer
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development processes. Modern software development increasingly relies on AI-based tools (e.g., large language models and autonomous agents) to generate, modify, and evaluate code. While these tools
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analyse mathematical models for collective movement using partial differential equations and / or agent-based approaches. The team This research will involve collaboration with applied mathematicians
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what alignment is required between the world models of agents and humans that collaborate. You also develop communicative strategies for the agent to detect misalignments and take actions to resolve