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with deep learning models such as autoencoders and neural networks. Experience with ecological, geospatial, or movement data (e.g., GPS telemetry). Strong oral and written communication skills, including
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learn how phenotypic datasets are integrated with genomic data for association analyses, genomic selection, and AI-driven methods, including machine learning and deep learning, to enhance germplasm
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-derived data sets, crop growth modeling, deep learning, and other statistical methods. The participant will learn through collaboration with a multi-disciplinary team of researchers to solve challenges
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pursues disruptive qubit research, innovative workforce development programs, and deep, collaborative partnerships to tackle some of the hardest open problems in quantum information science and technology