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Applications are invited for a PhD studentship in the Department of Computer Science at City, University of London. The successful candidate will work on Agentic Artificial Intelligence—the next
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to understand these dynamics. This project proposes a novel pipeline of ideas to generate tools and techniques to simulate HIV infection dynamics using a multiscale agent-based modelling technique (cells, viruses
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position within a Research Infrastructure? No Offer Description Want to explore how citizen collectives can drive societal change? Join us as a PhD in using AI-powered agent-based modeling to design adaptive
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structure. This PhD project will investigate systemic financial risks emerging from the convergence of agentic artificial intelligence and retail trading platforms. Using a combination of experimental
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
are recruiting PhD students to work on this exciting topic of LLM-based agentic AI, with the goal of making AI agents reliable, efficient, and collaborative. The research results are expected to become the core
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costs [1]-[5]. Building on these previous findings, this PhD project will design a new MARL architecture that incorporates model uncertainty, cyber-attack scenarios, and network reconfiguration events
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Your Job: In the CrowdING project, you will develop agent-based movement models that realistically simulate different behaviors such as lining up, overtaking, or pushing. Based on this, you will
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pipeline of ideas to generate tools and techniques to simulate HIV infection dynamics using a multiscale agent-based modelling technique (cells, viruses, drugs, antibodies, human lymph system, seconds, days
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programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural
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improvement options, and visualize for end users. Develop an agent-based digital twin platform of VvEs to support the energy transition decision making. Further develop agent-based model for collective decision