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is seeking a postdoctoral associate for a funded project on evolutionary responses to climate change and consequences for geographic range dynamics. Our project specifically focuses on improving
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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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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