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of Computer Science and Information Technology, to work on Insight Platform research in the area of LLMs and constraints – verifiable, reliable generative AI. In addition, the researcher may contribute to various other
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mobile telephony networks (5G, 6G, ORAN), satellite-based and airborne communication, or other wireless systems or networks AI, LLM and ML security, privacy and trustworthiness Anonymous or privacy
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developing and applying deep learning models, particularly in areas such as natural language processing (e.g. use of LLMs), computer vision (e.g. CNNs for image classification), and multimodal data integration
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, and enabling data-driven improvements in patient care. You will have opportunities to apply foundation models—including large language models (LLMs) to real-world clinical data. You will work with well
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research project management. Application Deadline: August 30, 2025 . Requirements for Post 2: Construction AI An excellent publication track record in BIM or AI; Substantial experience in LLM-related
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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | 2 months ago
, mathematics, and data science. Collaborative projects merge traditional geographic research with advanced computational methods such as graph neural networks (GNNs) and large language models (LLMs) to explore
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techniques will be used, including Large Language Models (LLMs). About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable
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Models for Multi-Modal Reasoning This project explores the integration of graph-based foundation models (e.g., knowledge graphs) with large language models (LLMs) to build AI systems capable of reasoning
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• Uncertainty quantification around LLMs • Constrained optimal experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing