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interoperable methodological framework for AI in gynecological oncology. We integrate symbolic knowledge representation (Ontologies/Knowledge Graphs) with Retrieval-Augmented Generation (RAG) and Large Language
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 6 hours ago
diversity and brings together expertise in software engineering, big data, clinical informatics, and medicine. Key Responsibilities Collaborate with researchers to design, develop, and refine large language
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that contribute to new breeding tools and accelerates discovery. The position will offer opportunities to explore several approaches to solve the optimisation strategies including simulation-based, big data
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instruments Analysing and interpreting data Writing experiment proposals, presenting results at conferences and publishing in peer-reviewed journals. Work in laboratories and large-scale facilities. Travel
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investigating adaptive neural circuits underlying distinct forms of behavioural flexibility. The postholder will be responsible for designing, developing, and operating advanced large-field or multi-brain region
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multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a team with a strong track record in Big Data analytics in child mental health, helping accelerate the move toward
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(IAS), Johannes Henn (MPP), and Daniel Baumann (NTU). More information can be found here . About LeCosPA LeCosPA is a research center at National Taiwan University (NTU). It was founded in 2007 with
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materials on research topic that may be presented to small and/or large groups. Actively participates in the preparation of required technical progress reports to research sponsors. Analyzes data using
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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-collection costs while maintaining validity for marketing and opinion research. Design, implement, and evaluate large-language-model (LLM) pipelines for synthetic data (fine-tuning, retrieval-augmented