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the reasoning and code-generation capabilities of LLMs and SLMs. The research will design autonomous agentic workflows to tackle complex networking challenges, including real-time network optimization, protocol
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groundbreaking symbiosis of cutting-edge AI combined with human support. To learn more please visit https://www.kcl.ac.uk/research/embrace About the role The Research Fellow in Digital Health & Data Sciences is
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to outputs generated by large language models (LLMs). The project aims to establish a benchmark of "gold-standard" CGM interpretations to inform safe, effective AI tools for diabetes care. The Research
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LLM Agents: Foundations, Attacks, and Defenses”. Your work assignments Large language model (LLM) agents represent the next generation of artificial intelligence (AI) sys- tems, integrating LLMs with
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worthiness of autonomous vehicles cyber risk management, advanced threat intelligence secure-by-design for IoT and policy governance of cybersecurity For more details, please view https://www.ntu.edu.sg/cysren
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) for the Romanian language ● programming skills in Python; knowledge of deep learning, machine learning, scikit-learn, Hugging Face, transformers, and LLMs Specific Requirements ● Collecting human-subject data using
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develop a framework based on collaborative AI concept, e.g., Agentic AI or other collaborative Large Language Models (LLMs), that synthesizes actionable intelligence from distributed, heterogeneous data
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Transparency). The main tasks will be to: Develop AI-assisted tools leveraging large language models (LLMs) to support community-based fact-checking Designi and evaluate methods to improve the robustness
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of tomorrow safer. Be part of change LLMs have gained significant attention recently due to their remarkable capabilities. A similar, yet less explored field focuses on text-to-image (T2I) model architectures
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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented