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research on model materials and/or materials with potential applications. The ICMCB is a UMR with an average of 280 agents (permanent and non-permanent), with 3 tutelles, hosted by the CNRS and in total ZRR
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volatile geopolitics. Shortages, trade frictions, and financial mismatches can stall otherwise viable tipping dynamics and establish carbon-intensive lock-ins. This PhD will develop an agent-based inspired
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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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latency constraints. - Federated and distributed learning for RAN hardware infrastructure management. o Knowledge of autonomous AI systems based on agents. Indicative skills/experience: - Multi-Agent
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carry out the doctoral thesis for the 26/27 academic year. Adoptive cell therapyagainst cancer is a type of immunotherapy that is based on the use of agents that enhance the immune cells of the patients
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volatile geopolitics. Shortages, trade frictions, and financial mismatches can stall otherwise viable tipping dynamics and establish carbon-intensive lock-ins. This PhD will develop an agent-based inspired
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) and an agent-based reinforcement learning system can developed to identify and optimise safe journeys to reach affected forest plantations. The specific research objectives will be co-developed with
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marine technology, together with more than 60 PhD students from all over the world. You will explore how emerging AI technologies—foundation models, generative design tools, agent platforms, reasoning
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studentship. Please apply via the King's Apply website (https://apply.kcl.ac.uk ) to the School of Cancer and Pharmaceutical Science (PhD). DO NOT CLICK ON THE APPLY BUTTON BELOW TO SUBMIT AN APPLICATION Follow
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studentship. Please apply via the King's Apply website (https://apply.kcl.ac.uk ) to the School of Cancer and Pharmaceutical Science (PhD). DO NOT CLICK ON THE APPLY BUTTON BELOW TO SUBMIT AN APPLICATION Follow