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developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
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University. This research opportunity will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including bulk and single
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research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease. Key
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into the brain's algorithms of perception and cognition while serving as a key resource for aligning artificial intelligence models with human-like neural representations. As part of this project, we are seeking
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will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA-seq, spatial transcriptomics and
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disease progression. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will
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visualizations and reports, and contribute to the development of predictive models and algorithms. The Data Scientist will be responsible for developing and adapting data collection and analysis methods
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departments. Duties include*: Collect, manage and clean datasets. Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data. Create databases and reports, develop
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and proposing the fundamental principles, algorithms, and implementations for solving high-level visual perception and cognition problems involving computational geometry, automated image and video