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Field
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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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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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to develop state-of-the-art multi-agent communication protocols and innovative educational technologies based on it. Further, in collaboration with a postdoc, the candidate should establish models
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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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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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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
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have focused on developing latency reversing agents as a first step to eradicate the latent reservoir and animal models are being studied to understand these dynamics. This project proposes a novel
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. The imperative is to move beyond task-specific AI and harness these foundational models for genuine network autonomy, realised through a collaborative ecosystem of specialised agents. This research will address
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continuous lifetime treatment. Recent efforts to cure HIV infection have focused on developing latency reversing agents as a first step to eradicate the latent reservoir and animal models are being studied
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
Large language models (LLMs) can read and write text and code, call tools, and follow instructions. They now allow us to build agents that plan and act over many steps instead of giving a single