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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will
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-aligned Intelligence and Novel Exploration) group (Prof. I. Bogunovic) at the Department of Mathematics and Computer Science, University of Basel, is inviting applications for multiple PhD positions in
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education to working life. The PhD supervisor will be Senior Researcher Ingunn Ness. Please contact her for more information about the project. About the work tasks: investigate how multi-agent ecosystems
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training, and interactions among autonomous entities can lead to vulnerabilities. These concerns are amplified in decentralized AI and multi-agent systems, when multiple parties (e.g., human or agent) might
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of new steel grades. To this end we will design agentic multi-modal models, whose components will be trained on legacy data, and which can be queried to provide a prompt-based summarization of relevant
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algorithms for large-scale or distributed training/Robustness, fairness, and personalization in multi-agent learning/Training efficiency and communication reduction/Distributed training of transformer models
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intersection with applications in, for example, autonomous systems, multi agent systems, and industrial use cases. You will be funded by and will join ELLIIT (Excellence Center at Linköping–Lund in
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University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Health Integrated Practice, Duke Primary Care, Duke Home Care and Hospice, Duke Health and Wellness, and multiple affiliations. Summary