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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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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
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automated reasoning tools such as ESBMC (https://github.com/esbmc/esbmc ) to address safety and reliability challenges in agentic reasoning systems. Where to apply Website https://www.findaphd.com/phds
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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will
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Food passage time through animal guts is a critical parameter for constraining an animal’s ability to act as an agent of nutrient, seed, microbe, or pathogen dispersal in its ecosystem. Current
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designing, developing and evaluating systems and models to enhance learning through AI technology. The PhD fellow will engage with developing and evaluating models and agents, as well as, multi-agent networks