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group (https://bckrlab.org). We focus on high impact applications and work on knowledge-centric AI and biomedical machine learning including multi-omics integration, single cell analysis, and sequential
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days 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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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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system for bacterial genomes using cutting-edge genomic language models. This project aims to adapt and extend transformer-based architectures to create a powerful tool for understanding and predicting
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networks, metabolic networks) to identify key disease drivers and biomarkers. Build predictive models for disease classification, patient stratification, and treatment response prediction. Collaborate with
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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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Sustainability in association with Professor Christina Lioma and her Machine Learning research team in the Department of Computer Science at the University of Copenhagen. The sub-package focuses
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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