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to engage in scientific activities both in Luxembourg and abroad, including seminars, summer schools, conferences, and more. The postdoctoral fellow will be working on developing multi-scale and multi-modal
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North Carolina A&T State University | Greensboro, North Carolina | United States | about 13 hours ago
, availability of resources, and the needs of the Department. We thus look for applicants that have a demonstrated track record in the applications of multi-agent systems. Programming and practical experiences
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opportunities to engage in scientific activities both in Luxembourg and abroad, including seminars, summer schools, conferences, and more. The postdoctoral fellow will be working on developing multi-scale and
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and tool-using agents for experiment design, simulation steering, data collection, and lab/compute orchestration; planning and memory; multi-agent collaboration. Scientific Reasoning: Program/path
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phase of expansion, supported by robust, multi-year funding, and seek colleagues who wish to tackle one of oncology's most persistent challenges in an environment offering genuine intellectual freedom
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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a postdoctoral researcher to work on a new project in the area of LLMs/multi-agent systems, under the direction of PI Professor Michael Wooldridge. The post is a full-time, fixed term position
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will be working on developing multi-scale and multi-modal models, contributing to bridge transport economics, network modelling and activity-based modelling, and leverage different types of (big) data
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is concerned with the challenging problem of modeling the complex modern radio environment, where a diverse set of devices and agents share the available spectrum. In this environment, it is crucial
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settings. We are seeking a highly motivated postdoc to conduct research into this fast-moving area. Directions may include investigating quality evaluation methods for multi-agent systems, attack surfaces