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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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learning, where agents share partial information to enhance resilience while preserving data/model privacy and communication efficiency. The project will also consider economic dispatch and energy trading
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microfluidics, ultrasound modeling and signal analysis. Three junior researchers will work synergistically to achieve a framework for improved image-based cancer diagnostics, consisting of an experimental model
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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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behaviours of multi-agent systems in response to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be
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university. We are willing to support applications related to our research on complex systems, network and data science, agent-based models, statistical physics, or computational and mathematical modelling
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? No Offer Description We have two exciting PhD positions at the intersection of formal software verification and Large Language Model (LLM) safety, focusing on extending state-of-the-art logic-based
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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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an interest (or prior experience) in computational intelligence, AI and/or agent-based modeling techniques. He or she will work closely with researchers in the Electrical Engineering department at TU Eindhoven
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to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be employed to achieve an appropriate trade-off