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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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://pritykinlab.princeton.edu ) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi-modal single-cell, spatial and genome editing
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discovery, scientific programming and genetic analysis. We encourage applications also from candidates with little background in biology or medicine, and a keen interest to learn. Recent publications: Clarke
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and implementation of data systems and analysis platforms with the platform development team (developers, data managers, scientists) Supporting strategic planning and integration of new data modalities
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research is based on large and high-dimensional datasets across multiple modalities, including molecular, clinical and histopathology imaging data. Our computational pathology research is based