Post doc to study transcriptomics of polyploidization
Susan Balenger is seeking a post-doctoral scientist to join an NSF-funded project for 1 year, and with possible extension, up to 3 years. The post doc will be based in the EEB department at UMN and is aimed at understanding how changes in gene dosage associated with polyploidization in the gray treefrog complex impact neuroendocrine regulation. This is a highly collaborative project, and the post doc with have extensive opportunities to work with co-PIs Chris Leary and Lainy Day at the University of Mississippi.
In collaboration with Balenger, the post doc will lead the assembly, annotation, and analysis of RNAseq derived datasets. The post doc will be expected to further integrate gene expression data with field collected hormonal phenotype data to connect regulatory and phenotypic changes following polyploidization events. Development of independent questions and projects utilizing samples and available datasets will be strongly encouraged.
The successful candidate will have the opportunity to be directly involved in field data collection from populations of frogs across the eastern United States, and to participate in broader impacts initiatives aimed at training high school, undergraduate, and graduate students in Mississippi. The post doc will be expected to present at scientific conferences and lead the preparation of manuscripts for publication in high-quality peer-reviewed journals.
Duties: 20% lead transcriptome assembly of gray treefrog species complex using long and short read data 70% lead transcriptome data analyses and manuscript preparation 5% perform RNA extractions from tissues and work with the UMN genomics core to coordinate sample preparation and submission 5% participate in field work activities and Pre-college summer programs
Required Qualifications: PhD in bioinformatics, evolution, neurobiology, genomics, endocrinology, or related fields Demonstrated experience with the assembly and analysis of transcriptomic data Ability to work independently Demonstrated experience with peer-reviewed publishing
Preferred Qualifications: Experience working with both long and short read RNAseq datasets Experience with extraction of RNA from tissue samples Strong writing skills
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