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Field
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) and Reinforcement Learning (RL) to acquire manipulation skills and conduct dexterous grasping. PhD candidate will explore the use of Large Language Models (LLMs) to guide task understanding, planning
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environment Interest in biological questions and disease mechanisms Basic understanding of opportunities and limitations of LLMs and transformer models Experience with genomics or transcriptomics, next
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of Large Language Models (LLMs) to guide task understanding, planning, and cross-domain knowledge transfer between robot types and manipulation scenarios by considering important challenges which include
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working in high‑performance computing (HPC) environments, and prior experience with post-training, fine-tuning, or training large language models (LLMs) is an advantage, but not a requirement. Experience
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, your mission will include: Develop a Mixture-of-Experts LLM agent framework tailored for network management tasks. Design agentic emergent communication and negotiation protocols for distributed AI nodes
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candidate has practical experience working with LLMs and demonstrate the actual work completed. We offer a generous annual leave package plus discretionary University closure days, excellent training and
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tasks Develop and apply NLP models and/or LLMs to identify and extract qualitative and quantitative impact information of floods, droughts, storms, and heatwaves in Europe and Africa since 1950 from text
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student to work within the ADaM project (Autonomous workflows for Data-driven first-principles Modelling). The project will leverage Large Language Models (LLMs) as active software agents to help automate
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P Agricultural AI data integration and management based on LLM. University Toulouse (FR) & University Padova (IT) Inclusivity We welcome applications from candidates of all backgrounds, genders, and
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generation and semantic search across historical statistics publications. Successful PhD candidates will extensively explore and analyse the suitability and potential benefits of LLMs (and other machine