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robust geospatial software tools for automated processing of satellite imagery and other remote sensing data. This includes implementing algorithms for geometric corrections, radiometric calibrations, and
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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outcomes ●casual representation learning for real-world data ● deep learning interpretation, fairness and robustness ●Regularly conduct computational experiments to execute algorithms on various health and
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document algorithms to detect and quantify disease-relevant patterns. • Contribute to both in-house and open-source software initiatives. Research Communication & Collaboration (20%) • Assist with
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faculty in developing theory and application tools for artificial intelligence (AI), and training efficient data analytics. 60% - Leading research in AI will include generative models, algorithms and
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tracked and the growth factor and exosome data will be compared using machine learning algorithms to identify growth factors and exosome numbers that correlate positively or negatively with clinical
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Rasch tables using time calibrations, and documenting and manualizing the algorithms and related products. The post-doctoral associate will be part time, 12-month employee of the University of Minnesota
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of probability, statistics and optimization. * Proven expertise in the implementation and testing of algorithms. * Strong programming skills in R or Python. * Familiarity with data science and visualization