Andrew Carnegie established a unique organization dedicated to scientific discovery “to encourage, in the broadest and most liberal manner, investigation, research, and discovery and the application of knowledge to the improvement of mankind…” The philosophy was and is to devote the institution’s resources to “exceptional” individuals so that they can explore the most intriguing scientific questions in an atmosphere of complete freedom. Carnegie and his trustees realized that flexibility and independence were essential to the institution’s success and that tradition is the foundation of the institution today as it supports basic scientific research.
Postdoctoral positions are available starting Sept 15, 2019 in the laboratories of Drs. Sue Rhee and David Ehrhardt at the Carnegie Institution for Science, Department of Plant Biology, to participate in an exciting DOE-funded project (https://dpb.carnegiescience.edu/news/2019/8/carnegie-led-initiative-receives-major-doe-biofuels-research-grant) to generate a cellular view of plant metabolism and use the information to build and test compartmentalized metabolic network models using Sorghum and Brachypodium. This project is part of a large DOE initiative (https://www.energy.gov/articles/department-energy-announces-64-million-research-plants-and-microbes) to expand knowledge of gene function in bioenergy crops.
The scientists will work in a team setting with personnel from Drs. Drs. Markita Landry (UC Berkeley) and Jenny Mortimer (LBNL) labs. Our aim is to first accurately define subcellular locations of enzymes by both experimental and computational approaches, then model the compartmentalized metabolic network under different environmental scenarios. Projects include: 1) developing experimental methods to identify subcellular locations of metabolic enzymes using carbon nanotube-mediated transient expression and confocal microscopy; 2) developing computational methods to accurately predict enzyme locations within the cell by leveraging the experimental data; 3) developing database and visualization infrastructure for making all the data and codes from this project available to the public; 4) developing compartmentalized metabolic network models to study the functions of Sorghum and Brachypodium metabolism in response to changes in water, light, and temperature; and 5) developing flux balance models and performing metabolic flux analyses to optimize the production of lipids, suberin, lignin and starch.
Successful candidates should have demonstrated ability for independent and critical thinking, excellent communication and teamwork skills, and enthusiasm for learning new things. Qualified computational biology candidates must have a Ph.D. or equivalent in bioinformatics, computational biology, plant biology, microbiology, systems biology, biochemistry or a related field, and a strong background in constraint-based metabolic model reconstruction of plants or fungi. Candidates should be proficient in at least one programming language, preferably python and/or perl. Experience in using MATLAB is highly desired. Qualified experimental biology candidates must have a Ph.D. or equivalent in cell biology, plant biology, genetics, microbiology, systems biology, biochemistry or a related field, and a strong background in molecular biology and imaging analysis using confocal microscopy.