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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
predictive modeling and pathfinding. This includes exploring the use of large language models (LLMs) for schema mapping and normalization tasks, evaluating embedding strategies that enhance interpretability
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drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models, and to develop user
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advanced machine learning and deep learning tools to decode the complexity of immune–tumor interactions, integrate multi-omics data at scale, and predict patient responses to therapy. The center works at
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding of neuroimaging data to predict subjective
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the project. Strong research profile in the applications of machine learning, artificial intelligence, multi-objective optimization, spatiotemporal modeling, and processing of satellite and high-frequency flux
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. Position Responsibilities Develop and implement machine learning and deep learning models to analyze and interpret high-throughput functional genomics data, such as ChIP-seq, RNA-seq, and ATAC-seq
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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups
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Computer Science, Biomedical Engineering, Bioinformatics, or a related field. Required: Strong understanding of machine learning (ML) and deep learning (DL) methods, with hands-on experience in model development and