Postdoctoral researcher to model pennycress metabolism in response to drought. We have developed computational pipelines to predict and annotate metabolic enzymes and pathways from sequenced genomes, which enables the generation of genome-scale metabolic networks. The successful candidate will leverage these resources in the Rhee Lab to model the metabolic networks field pennycress (Thlaspi arvense). Field pennycress is an overwintering biofuel crop related to both canola and arabidopsis. The harvested oilseeds have many uses including cooking oil, animal feed, and particularly biojet fuel. As an overwintering cover crop pennycress has many ecosystem services such as increasing biodiversity, preventing erosion, and sequestering carbon, all while being profitable for farmers to sell as biofuel. We aim to identify key metabolic reactions that are involved in drought resistance as well as in resource allocation between shoot and root tissues. The goal of the project is to investigate metabolic versatility against drought to inform future plant metabolic engineering and breeding efforts. This is a highly collaborative project among several pioneering labs in plant physiology, genetics, and computational biology (iPREP).
Qualified candidates must have a Ph.D. in Bioinformatics, Computational Biology, Biomedical Engineering, Biology, Plant biology, Microbiology, Systems Biology, Biochemistry, Computer Science, or a related field, and a strong background as demonstrated by journal publications in metabolic network reconstruction and analysis. The candidate will be familiar with analyzing constraint-based metabolic models and will have a strong interest in plant metabolism and biochemistry. Moreover, the candidate will be motivated to develop new computational approaches and/or algorithms to address novel biological questions. Candidates with prior experience in the use of MATLAB and/or the use of the COBRA toolbox are especially encouraged to apply. Working experience (either wet lab or dry lab) with plants and a proficiency in other programming languages such as R, Perl, and Python are pluses. The successful candidate should also be able to demonstrate independent and critical thinking, excellent communication and teamwork skills, and an enthusiasm for learning new things.
Equal Employment Opportunity Statement
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, citizenship, age, disability or protected veteran status.
Ph.D. in Bioinformatics, Computational Biology, Biomedical Engineering, Biology, Plant biology, Microbiology, Systems Biology, Biochemistry, Computer Science, or a related field
journal publications in metabolic network reconstruction and analysis
familiarity with analyzing constraint-based metabolic models and a strong interest in plant metabolism and biochemistry
experience in the use of MATLAB and/or the use of the COBRA toolbox
working experience (either wet lab or dry lab) with plants and a proficiency in other programming languages such as R, Perl, and Python
Required Application Materials
CV, cover letter, contact details for 3 references.
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