Requires Masters in Statistics, Data Science or closelyrelated field 1 yr experience training, validating and applying genomic prediction models for applied plant breeding pipelines; fitting generalized linear mixed models to large data sets using R Python; building IT infrastructure for cloud computing; designing, developing implementing machine learning and deep learning algorithms using R and Python; using statistical machine learning packages, including Glmnet, SciPy, Statsmodels, Pandas, Numpy, Scikitlearn, Tensorflow, Keras, PyTorch andor PySpark; aggregating summarizing datasets with GCP BigQuery, Presto, Superset and AWS RedShift; building optimizing genomic prediction workflows using AWS, Google Cloud, parallel computing libraries, including joblib, multiprocessing, parallel andor doParallel with foreach; implementing genomic prediction workflow orchestrations using Docker Airflow; analyzing visualizing data to develop R Shiny and Python Dash in Flask web applications using Domino, DataRobot, RStudio and RStudio Connect; and using genomics selection, QTL mapping, genomic wide association analysis, selection theory, quantitative trait dissection molecular breeding methods to analyze genomics data and generate genomics predictions.

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