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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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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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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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institutions, including Politecnico di Torino, to advance scientific understanding and drive innovation through agent-based models, population models, and software implementations. This unique opportunity allows
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background in individual- /agent-based modelling Experience with modelling of animal energetics Strong R and Netlogo skills Good understanding of movement & population ecology Experience in publishing
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with agent-based models, statistical methods, artificial intelligence, process modeling, and data visualization Experience working in teams with people from various socioeconomic, cultural, and
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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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to contribute to improving the quantitative accuracy of multimodal data. PhD Work Plan In the initial phase, the candidate will conduct a literature review on MR-based attenuation correction methods for PET