We seek a postdoctoral researcher to work on a project investigating the role of network biology and quantitative genomic variation (both host and pathogen) in controlling the evolution of plant/biotic interactions across eudicots. Our laboratory has been conducting extensive work to understand the molecular basis of quantitative phenotypes at a genomic level utilizing network biology in Arabidopsis thaliana and the necrotrophic plant pathogen (Botrytis cinerea) (for a list of publications please go to the Kliebenstein Google Scholar page). Previously, we have developed co-transcriptomic methods to measure the host and pathogen transcriptomes in the same sample and identify interacting networks.

In this project, we will complete the long-read sequencing of 100+ pathogen isolates and conduct host-pathogen co-transcriptomics across 16 different eudicot plant species. The goal is to identify shared and lineage specific networks that control the host response to the pathogen and validate the networks effects on host plant responses to biotic attack. Using these approaches, we will begin to understand how plant networks evolve and adapt at the species, family and genera level. Further, we will specifically investigate how the plants specialized metabolism influences these processes. These questions are directly translatable to fundamental population biology theory, ecological theory.

In addition to fundamental insights, this work will have direct application to improving crop productivity by developing a deeper knowledge of how networks evolve which is critical to translate information from one species to another. Thus, there is an extensive collection of phenotypic and genomic data that are ready to be analyzed and utilized to develop and test new hypothesis surrounding quantitative disease interactions. The candidate will also be encouraged to develop their own independent research ideas and avenues on any component of plant-biotic interactions.

The project is currently funded until August 2025. Applicants with a Ph.D. in quantitative or computational genetics will be given high priority. Knowledge of modern molecular biological techniques (e.g. PCR, Sequence analysis, in vitro protein expression etc.) and laboratory-based research is required. The position is available starting in December 2022 but the start date is flexible. Salary will be commensurate with experience; the position also includes health insurance and other benefits.

The laboratory is an interactive group at the forefront of applying genomics techniques to understanding the molecular basis of quantitative phenotypes in both an ecological and applied context. We have numerous international collaborations and opportunities with an excellent track record of placing individuals in advanced positions.

To apply, please e-mail a cover letter, curriculum vitae, and the names of three references to Daniel Kliebenstein. Applications will be reviewed until the position is filled. For more information contact Dr. Kliebenstein via phone at (530) 754-7775 or via e-mail.

Email to Dr. Kliebenstein may be routed via the Help Email address


Basic qualifications(required at time of application)

-A Ph.D in Genetics, Plant Pathology, Microbiology, Biochemistry or related fields

  • Wet lab genomics skills in transcriptomics
  • Computational analysis skills for genomics and statistics
  • Plant Pathology Experience or other Plant Biotic interactions

Application Requirements

Document requirements

  • Curriculum Vitae – Your most recently updated C.V.
  • Cover Letter

Reference requirements

  • 3 required (contact information only)

Apply link:https://recruit.ucdavis.edu/JPF05063

Help contact: kliebenstein@ucdavis.edu

Next review date: Tuesday, Aug 9, 2022 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.

Final date: Friday, Aug 12, 2022 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.

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