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Field
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of Distributed Mixture-of-Experts (MoE) and Small Language Models (SLMs) to create autonomous, intent-driven networks. This isn't just about connectivity; it’s about building a collaborative, agentic ecosystem
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, abandonment, comments, peer interaction) Formalization of algorithms for orchestrating educational AI agents : Train RL and LLM agents and study multi-objective optimization (mastery, well-being stability) Work
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-enabled adaptation. The aim is to develop theoretically grounded yet practically deployable algorithms that allow multi-agent robotics to operate robustly in dynamic, uncertain, and interactive environments
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area of AI security, on the topic of “Memory Poisoning in LLM Agents: Foundations, Attacks, and Defenses”. Your work assignments Large language model (LLM) agents represent the next generation of
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(UNIBO, Italy) Supervisors: Prof. Anna Guerra, Dr. Francesco Guidi Project Context: Autonomous agents navigating complex environments rely on sensed data to build internal representations of the world and
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fungal infections. Emerging research is exploring radiometals conjugated with targeting molecules for in vivo imaging of infections via PET or SPECT. These agents can also be linked to therapeutics
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approved drugs. Candidate drugs from this screen, the literature and from repositories (GDSC, CellMiner, CTRP, DepMap/PRISM, PharmacoDB) are currently being validated as single agents and in synergy assays
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administrative agents, about 150 PhD students. The lab is very open to international collaborations; more than 100 foreigner scientists coming from 20 different countries are currently working
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from this PhD project into an agent-based model. This model will be developed by other PhDs in the project team and simulates household adaptation behaviour over time in global flood-prone regions
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experimental error. Thus, if AI systems are supposed to perform research autonomously, they need to have error-reasoning ability. This fully funded PhD Studentship forms part of the Error-Reasoning Agents (ERAs