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in the University of Minnesota. The research will focus on applying, developing and implementing novel statistical methods for causal inference, integrative data analysis or/and machine/deep learning
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communication and collaborative skills. Experience with SLAM, sensor fusion, LiDAR/depth camera data processing. Familiarity with deep learning for obstacle avoidance (e.g., map-less navigation). Background in
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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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weekly working time of 40 hours per week. The position can be filled on a part-time basis. Background: Addressing climate change and biodiversity loss requires a deep understanding of global land-use
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learning, or deep learning models Salary Information Commensurate with experience Review Date March 10, 2025 Additional Information The successful candidate will be required to have a criminal conviction
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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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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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, 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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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