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functional data analysis, tensor regression, high-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated
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skills. Excellent programming skills in Python and Julia with experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Experience building complex software systems, preferably with industry
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ability to analyze large datasets Knowledge of coastal and nearshore processes Preferred Qualifications: Proficiency in statistical modeling and time series analysis Experience with machine learning or deep
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in theoretical physics and mathematical physics, and the development of mathematical or physical theory of deep learning. The researcher will perform cutting-edge research in an intellectually
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past
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sciences education and a deep commitment to excellence in scholarship and all forms of creative and intellectual expression, with sponsored research expenditures totaling more than $38.6M per fiscal year
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, Coastal Marine Science, Computer Science, Electrical Engineering, or a closely related field. The ideal candidates will have experience in one or more of the following topics: deep learning for image and
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turbulent combustion applications, as well as parallel scientific computing. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and
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with a team. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience in machine learning/deep learning methods
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bodies down to the bottom of the deep sea. The Aquatic Life Foundation Project (AqQua ) will, for the first time, combine billions of images acquired with a variety of devices across the globe for large