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. They work together to enhance our understanding of intelligence, cognition in autonomous agents, and teams of agents in the context of mediatised worlds. The insights are then transferred into a new
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of responsible human-AI collaboration. The successful candidate will become a member of the Multi-Agent Systems Group, Artificial Intelligence Department and will work under the supervision of Dr. Jakob Schoeffer
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Howard Taylor Ricketts, William H. Taliaferro, Lowell T. Coggeshall, William Burrows and James Moulder. Howard Taylor Ricketts identified rickettsiae as the causative agents of Rocky Mountain Spotted Fever
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and data-driven models of sarcomere assembly. This will involve mean-field models and agent-based simulations. Additionally, depending on your aptitude, you can analyze topological defects of smectic
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on natural language processing tasks (NLP), initially mostly on advanced information extraction, but recently also covering other topics, such as conversational agents. In this PhD you will focus on designing
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develop data-inspired and data-driven models of sarcomere assembly. This will involve mean-field models and agent-based simulations. Additionally, depending on your aptitude, you can analyze topological
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Institute for Mathematics, Computer Science and Artificial Intelligence with the topic of responsible human-AI collaboration. The successful candidate will become a member of the Multi-Agent Systems Group
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of vaccines and antiviral agents. These studies often involve interdisciplinary collaboration combining virological, veterinary, pathological, and immunological expertise. Your tasks will include: • Preparing
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Applying guide Study abroad and exchange Events Ask us a question Agent resources Short courses and professional education Short courses and professional education Bringing together knowledge from across
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at the intersection of user modelling, multi-agent systems, simulations and modelling, reinforcement and deep learning, evaluation and responsible AI. We understand it is unlikely someone will have a background in all