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-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated evidence of excellent programmin g, collaboration
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centrifugal, digital, capillary, pressure, or microvalve-based microfluidics. Experience in deep-learning and artificial intelligence in the field of microfluidics to support applications such as high
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-of-the-art methods for evaluating treatment effects from randomized and observational data sources that are subject to multiple forms of bias due to, for example, missingness, censoring, irregular assessment
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