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Field
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-agent systems. Methodology: Integrate ToM models with Reinforcement Learning based frameworks for single-agent and multi-agent decision-making. Develop simulation environments capturing realistic human
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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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, Mathematics, Computer Science, or a related quantitative field Have strong modelling, computational, and code development skills Have experience with network science, multi-agent systems, statistical mechanics
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– Adaptive & Agentic AI. The PhD project focuses on developing robust and reliable machine learning systems that can adapt at test time under real-world distribution shifts. Modern foundation models (e.g
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platforms such as Unity, Unreal, or Godot Up-to-date knowledge of recent advances in generative AI, including vision-language models (VLMs) and agent-based AI systems Ability to design and conduct experiments
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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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of genetic loci and chromosomal rearrangements. • Develop and analyze individual-based (agent-based) models programmed in SLiM or C++ to test the robustness of analytical results (e.g., accounting for genetic
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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