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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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particularly close partner: it houses UTEP's Master of Science in Artificial Intelligence (M.S. in AI) program and includes faculty whose research intersects with neuroscience, biomedical imaging, and
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(e.g. extreme value analysis) to identify patterns of marine extremes and their spatial and temporal characteristics; developing machine and deep learning models (e.g. convolutional neural networks
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the physio-pathological conditions, as an aid in the clinical diagnosis. Objectives: This doctoral project aims to develop deep learning neural networks to investigate data in available database (e.g
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architectures which leverage our increasing understanding of the behaviour of neural networks trained with DP to ameliorate these trade-offs in biomedical applications. - Foundations of private machine learning