Design and execute genomics experiments, analyses, pipelines, and workflows to solve complex biological problems for pest resistance management; Identify opportunities for innovation and improvements in discovery and management of pest resistance using genomics data; Collaborate with multi-disciplinary teams including lab and field biologists, biochemists, and data scientists to translate research questions into relevant analyses and goals; Design and develop new data science tools that link genomic and transcriptomic factors to novel traits and resistance mechanisms
Requirements:
PhD in Bioinformatics, Computational Biology, Data Science, Statistics or a similar field. A strong M.S. candidate with at least 3 years of relevant professional experience can be considered; Strong experience in Python or R programming languages for statistics or scientific programming; Proficiency in computational genomics and analyses of comparative genomics datasets is required; Experience processing and analyzing large-scale genomics population variation data and whole genome transcriptomics experiments
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Data Scientist - Computational Genomics Design and execute genomics experiments, analyses, pipelines, and workflows to solve complex biological problems for pest resistance management; Identify opportunities for innovation and improvements in discovery and management of pest resistance using genomics data; Collaborate with multi-disciplinary teams including lab and field biologists, biochemists, and data scientists to translate research questions into relevant analyses and goals; Design and develop new data science tools that link genomic and transcriptomic factors to novel traits and resistance mechanisms PhD in Bioinformatics, Computational Biology, Data Science, Statistics or a similar field. A strong M.S. candidate with at least 3 years of relevant professional experience can be considered; Strong experience in Python or R programming languages for statistics or scientific programming; Proficiency in computational genomics and analyses of comparative genomics datasets is required; Experience processing and analyzing large-scale genomics population variation data and whole genome transcriptomics experiments
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