Computational Biology Postdoctoral Researcher

Carnegie Institution for Science
Plant Biology
United States CA Stanford


A postdoctoral researcher position is available immediately (May 2021) in the laboratory of Dr. Sue Rhee ( in the Department of Plant Biology at the Carnegie Institution for Science in Stanford, CA.

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 of C4 plants such as Setaria and Sorghum species as well as a desert extremophile Tidestromia oblongifolia in order to identify key metabolic reactions that are involved in biomass production as well as in resource allocation between shoot and root tissues. We are particularly interested in modeling metabolism under under water limiting and high temperature conditions. The goal of the project is to investigate metabolic versatility against drought and heat stress in key C4 plants 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 (


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.

Start date

As soon as possible

How to Apply

Please apply online at: