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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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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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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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. Role Requirements The GTA roles are based at the Edinburgh campus and successful applicants will be expected to work at that campus. A GTA PhD scholarship is a four-year fixed term position: Students
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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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Energy Modelling”. The PhD candidate will be supervised by supervisors from DTU and Chalmers. This project will integrate energy technology, building physics, HVAC systems, control systems architecture
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scenarios. Your research will map multilevel governance structures and you will co‑create mitigation strategies through participatory workshops. You will model farmers’ adaptation behaviour using agent‑based
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on correlation-based machine learning. When an agricultural system fails due to compounding climate extremes - like a simultaneous heatwave, drought, and ozone pollution spike - standard models can forecast the
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discovery methods to enable a search for explanatory multi-level agent-based models that can be calibrated to - and validated against - such empirical phenomena. Funding Notes This is a self-funded research