Department: DPB – Plant Biology
Location: Stanford, CA
Carnegie Institution for Science, Department of Plant Biology, Stanford, CA 94305
Rhee Lab – https://dpb.carnegiescience.edu/labs/rhee-lab
Moi Exposito-Alonso Lab – www.moisesexpositoalonso.org
Starting: immediate start possible; Deadline: until filled
The Rhee and Moi Labs aim to recruit a highly motivated and creative person with strong training in bioinformatics / quantitative genetics / evolutionary biology / computer science.
This project seeks to develop predictive models of causal genes of plant traits, particularly in the model C4 grass Setaria and bioenergy crop Sorghum. This project has opportunities to work with leaders and trainees in plant biology ranging from evolutionary biology, genetics, genomics, phenomics, physiology, and synthetic biology (www.foxmillet.org). The successful candidate will use machine learning models and genome-wide associations of plant functional and genomic data (RNAseq, sequence polymorphisms, evolutionary signatures, and functional annotations), both publicly available and newly generated in our consortium for Setaria and Sorghum, to discover and predict the causal effect of genes controlling metabolic, developmental, and fitness traits in stressful drought and high-density planting conditions.
This position involves conducting research independently, being involved in collaborative projects, preparing publications, and presenting research in scientific meetings. The candidate will also work with computational postdocs in the consortium who have built baseline algorithms using different frameworks, and experimental postdocs who will test the predicted causal genes using genome editing and physiological measurements. We prefer candidates interested in strengthening connections between molecular ecological genetics, sustainable breeding, and computational biology, who will be active members of the research community at the Carnegie Plant Biology and the Stanford Biology departments.
This is a full-time position with competitive salary and benefits. The labs are located at the Carnegie Institution on Stanford campus. Carnegie Postdocs have access to most Stanford facilities. Stanford campus is a vibrant community embedded in the San Francisco Bay area, with opportunities for extensive social and scientific interactions. The initial position will be for one year with potential renewal of up to three years depending on performance.
Qualifications: 1) a Ph.D. or equivalent in Computer Science, Biology, Bioinformatics, Mathematics, Engineering or related field; 2) expertise or fluency in large-scale data analysis, statistics, genomics, machine learning, and/or related field; and 3) proficient in programming. Candidates with experience in developing machine learning algorithms and/or trained in quantitative genetics are especially encouraged to apply. The successful candidate should have a demonstrated ability for independent and critical thinking, ability and initiative to learn new things, excellent communication and teamwork skills and passion for biological research.
About Carnegie: Carnegie Institution for Science is a U.S.-based non-profit, private endowment. Andrew Carnegie founded the Carnegie Institution of Washington in 1902 as an organization for scientific discovery to serve as a home to exceptional individuals – women and men – with imagination and extraordinary dedication capable of working at the cutting edge of their fields. investigators are leaders in the fields of plant biology, developmental biology, Earth and planetary sciences, astronomy, and global ecology. The Department of Plant Biology engages in basic research on the mechanisms involved in plant and algae growth, development, and evolution (https://dpb.carnegiescience.edu). Carnegie is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, sex, sexual orientation, gender identity, age, veteran status, disability or any other protected status in accordance with applicable laws.