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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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candidate for the UPC-2 research project "Multi-agent multi-objective agent-based modelling for automatic distributed optimization of cellular networks" at the Universitat Politècnica de Catalunya (UPC
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tailored physicochemical and spectral properties. This PhD project focuses on the development of innovative contrast agents suitable for both CT and SPCCT. For this project, the advanced contrast agents will
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synthesizing findings with prior knowledge from the scientific literature, a process that today depends heavily on manual expert interpretation. Recent advances in large language models, agentic systems, and
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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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-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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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