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Areas (Codes 25–29) 1. Machine Learning (Code 25) Objectives: Support UFABC’s undergraduate and graduate programs, strengthen research in Machine Learning, and expand English-taught course offerings
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–frequency representation (local stationarity, correlations, textures, extrema, anisotropy). This approach suggests leveraging the entire representation to define more robust detection and tracking criteria
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: Genetic and developmental mechanisms of cardiovascular disease. Goals: Discover causal variants, understand congenital heart disease, and advance pharmacogenomics. Heart Failure Focus: Mechanisms and
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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integrate sophisticated AI systems, rigorously testing, validating, and tracking learning models, and troubleshooting issues to ensure system accuracy and reliability. A core objective of this role is to
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, detection, care, and training. MCC will make a global impact on improving health by reducing cancer burden through accelerated discovery and translation, compassionate and interdisciplinary care, education
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their academic and career goals while advancing the University’s strategic plan objectives. CSUF strives to retain all faculty by providing resources to build meaningful connections and community within and across
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Postgraduate online event: Tuesday 4 November 2025, 9am – 5pm Chat to staff, students and academics to discover what it's like to study at our world changing campus as a postgraduate student at UofG
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Postgraduate online event: Tuesday 4 November 2025, 9am – 5pm Chat to staff, students and academics to discover what it's like to study at our world changing campus as a postgraduate student at UofG