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will focus on integrating image processing, machine learning, and deep learning techniques to improve the characterization and modeling of reservoir rocks. In the first stage, CT, NMR, and BHI images
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to be developed by the postdoctoral researcher will focus on proposing an innovative approach based on semantic segmentation with deep neural networks for the identification of multiple facies classes in
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-doctoral fellowship starting January 1, 2026. The project “LSST – A New Window into the Be Phenomenon” will use the deep, long-term and high-cadence photometry from the Vera C. Rubin Observatory/LSST
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Phenomenon” will use the deep, long-term and high-cadence photometry from the Vera C. Rubin Observatory/LSST to investigate rapidly rotating B-type (Be) stars in the Milky Way. Goals include: (1) identifying
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., decision trees, logistic regression, Naive Bayes, SVM); Artificial neural networks and deep learning; Dimensionality reduction (e.g., PCA, Johnson-Lindenstrauss); Classical methods for clustering (e.g., k
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integration of local opportunities and needs into global strategies. Who You Are You are an experienced leader with a deep understanding of clinical operations and trial delivery. You thrive in dynamic