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We are seeking a motivated and creative PhD student to explore safe and trustworthy planning under uncertainty in multi-agent systems. They will collaborate on interdisciplinary research which draws
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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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operationalizing next-generation LLM solutions that enable ING to streamline core processes, strengthen compliance, and scale agent-based AI systems. The position potentially targets at one of the following three
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into different types of neurons. In this PhD project we will work to further develop these human stem cell-based models as a platform for robust and reliable identification of neurotoxic agents. To achieve
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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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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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at the interface of biological physics, agent-based simulations and machine learning to turn quantitative imaging data into a mechanistic, testable model of spindle positioning. In particular, we expect
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learning with knowledge-based inference, validated by independent experiments and partially supervised by human-in-the-loop systems. A key question will be how agentic AI and foundation models can be
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, physical limitations). Their activities will include: - Design and analysis of mathematical models of multi-agent systems, with an emphasis on stability, controllability, and synchronization. - Obtaining
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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