We are looking for a postdoc scientist to join Carol Huang’s lab at New York University (https://huanglab.rbind.io) in the heart of Manhattan.
ABOUT THE LAB
Our lab uses both “dry”- and “wet”-lab genomics methods to study gene regulation at the systems level. Specifically, we are interested in understanding how the repertoire of genome and epigenome variations at the levels of individual and population give rise to phenotypic variations in realistic environment. The extensive intra-specific genome and epigenome variation and adaptability to the environment found in the plant kingdom provide rich resources to investigate this question. Working with the reference plant Arabidopsis and extending to agriculturally and ecologically important plants, we focus on three related topics:
- identifying genomic and epigenomic determinants of intra-specific transcriptional variation;
- using single cell sequencing methods to characterize cell type-specific responses;
- mapping population- and cell type-specific regulatory networks in hormone responses.
We aim to build a highly interdisciplinary and collaborative lab, and are committed to provide a supportive environment for lab members to achieve scientific excellence and gain expertise in both computational and experimental skills. The lab space is centrally located in the Washington Square campus of NYU, in a six-year-old building that houses the Center for Genomics and Systems Biology.
ABOUT THE POSITION
We are looking for a motivated scientist with expertise in any of the areas listed below, as evidenced by first-author or co-first-author publications. We seek candidates with PhD training in plant biology, molecular biology, genetics/genomics, ecology/evolution, or computational biology. Experience with Arabidopsis and/or plant hormone biology is a plus. Relevant skills and experience include work on molecular cloning, protein expression and purification, single cell genomics, comparative genomics, analysis of high-throughput sequencing data, basic statistics, computer programming and/or machine learning. The position is expected to continue for multiple years contingent on satisfactory performance.
How to apply
Please send a brief description of research interests and accomplishments, CV, contact information for three references, and a publication representative of your work to firstname.lastname@example.org. We encourage candidates to identify potential funding sources for which they may be eligible and interested in applying for. Applications will be considered until the position is filled. Informal inquiries are welcome.