美国普渡大学植物遗传学和基因组学研究博士后职位

Plant Genetics and Genomics Potsdoctoral Research Associate

Job Description

Postdoctoral Research Associate Position

Research involves genomic and phenotypic approaches in the genetic study of maize tassel and ear inflorescence architecture. Funding is from a multi-institutional NSF Plant Genome Research Program grant on natural variation and drought responses in developing maize inflorescences. Different approaches are being pursued in parallel. Fine mapping of tassel branch number and ear row number QTL using heterozygous inbred families (HIFs) derived from the Nested Association Mapping Population (NAM) has precisely defined a number of QTL intervals, revealing candidate genes. A very large phenotypic dataset of inflorescence architectural traits has been collected on the Ames panel, which consists of over 2500 maize inbreds, grown in multiple environments. This data set is ready for in-depth Genome Wide Association Study. The two genetic resources are intended to be complementary as candidate genes present in fine mapping intervals will also be examined for association in the Ames panel through GWAS. This may involve some gene specific marker development for candidate gene analysis.

This position is based in the Department of Agronomy and involves lab work in Lilly Hall, field work at Agronomy Center for Research and Education, and use of core facilities on Purdue campus. There will be interaction with the other labs that are part of the multi-institutional NSF grant and are studying drought stress effects on inflorescence development. There may be some winter nursery activities and outreach activities, and possible travel for participation in field and greenhouse drought studies and the annual grant group meeting.

The overall objective of this research is to gain a better understanding of the genetic basis of inflorescence architecture, which will complement studies on effects of early season drought on developing inflorescences.

Qualifications: A PhD is required, with flexibility in discipline, which could be an agricultural, statistical, or biological area. A strong background in statistics and quantitative genetics is one suitable background. A strong background in molecular genetics and wet lab skills and experimentation is also suitable. Various backgrounds paired with willingness to learn is also suitable. The project will involve statistical analysis and association of marker based data with field phenotypic data, thus there is opportunity to learn and grow in an area that the successful candidate has less expertise. Some RNAseq analysis may be performed as logical, or analysis of RNAseq data created in other labs may be performed. There will be opportunities to attend workshops and short courses to learn complementary and useful skills and expertise.