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incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non
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: 10.1101/2025.09.08.674950), and AI/machine learning. We work closely with clinicians to translate our findings into clinical practice, focusing on genomically complex sarcomas and haematological
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statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft matter and nanomaterials
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the Job related to staff position within a Research Infrastructure? No Offer Description Postdoc in Machine Learned Semiconductor Material Properties for Quantum Transport Simulations The simulation
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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Intelligence (AI) and Machine Learning (ML) methods to tackle complex biomedical challenges in nutrition and health. This is a one-year full-time benefits-eligible position that may be extended for up to four
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in
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computational modeling to identify bacterial strains and metabolites that promote or hinder probiotic establishment. By combining multi-omics data with systems biology and machine learning approaches
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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monitoring. Familiarity with computational image analysis, scripting (Python, MATLAB), or machine learning–based image workflows. Experience with method development, imaging assay optimization, or pipeline