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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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more than 200 people, of which about 90 are PhD students, and about 40 % of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's
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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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consortium also includes industrial and clinical partners. This particular PhD project aims to develop the in-vitro vasculature-on-chip models, microfluidic chips in which controllable and physiologically
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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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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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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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systems and models to enhance learning through AI technology. The PhD fellow will contribute to the Technological Advancement cluster by advancing synthetic data generation as a key work of AI LEARN’s
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to evolve advanced, human-centered AI technology to empower human learning, including designing, developing and evaluating systems and models to enhance learning through AI technology. The PhD fellow will
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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