A postdoctoral researcher position is available immediately (January 2018) in the laboratory of Dr. Sue Rhee (https://dpb.carnegiescience.edu/labs/rhee-lab) in the Department of Plant Biology at the Carnegie Institution for Science in Stanford, CA.
The Rhee Lab has developed computational pipelines to predict and annotate metabolic enzymes and pathways from query protein sequences, which allows for generating genome-scale metabolic networks. The successful candidate will leverage these resources in the Rhee Lab to model the metabolic networks of Setaria and Sorghum species in order to identify key metabolic reactions under water limiting conditions that are involved in biomass production as well as in resource allocation between shoot and root tissues. The goal of the project is to investigate metabolic versatility against drought stress in Setaria and Sorghum species to inform future plant metabolic engineering and breeding efforts. This is a highly collaborative project among several pioneering labs in plant physiology, genetics, engineering, and computational biology (http://foxmillet.org).
The Carnegie Institution, a private, nonprofit organization engaged in basic research and advanced education in biology, astronomy, and the earth sciences, was founded and endowed by Andrew Carnegie in 1902 and incorporated by an act of Congress in 1904. Andrew Carnegie conceived the Institution’s purpose “to encourage, in the broadest and most liberal manner, investigation, research, and discovery, and the application of knowledge to the improvement of mankind.” The Department of Plant Biology engages in basic research on the mechanisms involved in the growth and development of plants and algae. The Department of Plant Biology is co-located with the Carnegie Department of Global Ecology on a seven-acre site on the campus of Stanford University.
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.
Please apply online at: