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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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of the following areas: distributed artificial intelligence, coordination and negotiation, game theory and mechanism design, multi-agent learning and reinforcement learning, agent-based modelling and simulation
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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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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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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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controllability • Learning and calibration strategies for uncertainty-aware language model prediction • Knowledge-augmented and neuro-symbolic approaches for language-based reasoning • Evaluation and design of LLM
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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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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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the supervision of PhD or Master students. The recruited person will exploit the existing test benches (Beta source and infrared laser) developed for the first EICROC protype (EICROC0, 4x4 channels) to characterize