UNC
Overview
The Renaissance Computing Institute (RENCI) at UNC Chapel Hill is seeking a MATRIX Postdoctoral Research Associate to join our interdisciplinary team at the forefront of biomedical data integration and AI-driven discovery. Responsibilities
Design and implement sophisticated data ingestion and transformation workflows that unify diverse biomedical sources into structured, queryable knowledge graphs. Support the creation of FAIR-aligned metadata (including emerging standards like Croissant) to ensure data provenance, accessibility, and reuse across translational science domains. Shape infrastructure that enables expressive querying (e.g., via the Model Context Protocol) and optimize integrated data for downstream AI/ML applications such as predictive modeling and pathfinding. Explore the use of large language models (LLMs) for schema mapping and normalization tasks, evaluate embedding strategies that enhance interpretability, and develop novel approaches that use AI and knowledge graphs to make novel predictions and answer complex user queries. Contribute to open-source tools, co-author publications, and present research at national and international venues. Context and Team
As part of a dynamic, collaborative team operating at the intersection of biomedical informatics, semantic technologies, and computational discovery, you'll contribute to impactful, cutting-edge tools and methods that advance scientific understanding and translational outcomes.
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The Renaissance Computing Institute (RENCI) at UNC Chapel Hill is seeking a MATRIX Postdoctoral Research Associate to join our interdisciplinary team at the forefront of biomedical data integration and AI-driven discovery. Responsibilities
Design and implement sophisticated data ingestion and transformation workflows that unify diverse biomedical sources into structured, queryable knowledge graphs. Support the creation of FAIR-aligned metadata (including emerging standards like Croissant) to ensure data provenance, accessibility, and reuse across translational science domains. Shape infrastructure that enables expressive querying (e.g., via the Model Context Protocol) and optimize integrated data for downstream AI/ML applications such as predictive modeling and pathfinding. Explore the use of large language models (LLMs) for schema mapping and normalization tasks, evaluate embedding strategies that enhance interpretability, and develop novel approaches that use AI and knowledge graphs to make novel predictions and answer complex user queries. Contribute to open-source tools, co-author publications, and present research at national and international venues. Context and Team
As part of a dynamic, collaborative team operating at the intersection of biomedical informatics, semantic technologies, and computational discovery, you'll contribute to impactful, cutting-edge tools and methods that advance scientific understanding and translational outcomes.
#J-18808-Ljbffr