The Laboratory of Dr. Chaolin Zhang in Department of Systems Biology, Columbia University Irving Medical Center (CUIMC) has multiple postdoctoral openings to conduct NIH-funded research on mammalian RNA regulatory networks. This posting is to search for candidates in the field of Computational or RNA Systems Biology.
Taking a multidisciplinary approach that tightly integrates biochemistry, molecular biology, genome engineering and high-throughput data analysis and integrative modeling, the Zhang Laboratory studies how RNA and proteins interact to form regulatory networks in the nervous system at the mechanistic and systems levels, how these networks contribute to intrinsic neuronal functional properties, and how such properties are implicated in health and disease. We are working to translate fundamental discoveries to RNA-based precision medicine for devastating disorders with unmet medical needs. The Zhang lab consists of a group of inspired and creative scientists from diverse background. Recent lab members have successfully transitioned into prominent academic and pharmaceutical industry positions. More information about the Zhang laboratory can be found at zhanglab.c2b2.columbia.edu.
The candidates will participate in and lead exciting projects that aim to understand fundamental mechanisms of RNA-protein interactions and alternative RNA splicing regulation in normal and disease contexts. Innovative computational and machine learning-based approaches will be used to develop predictive models for analysis of high-throughput genomic data, including large scale bulk/scRNA-seq and CLIP-seq in various cellular contexts, as well as other genomic and genetic variant datasets. The candidates will work in a dynamic environment and also work closely with experimental biologists. Strong mentorship will be provided to help them achieve their career goals.
Selected recent publications:
1. Feng, H., Moakley, D.F., Chen, S., McKenzie, M.G., Menon, V., Zhang, C. 2021. Complexity and graded regulation of neuronal cell type-specific alternative splicing revealed by single-cell RNA sequencing. Proc. Nat. Acad. Sci. USA. 118: e2013056118.
2. Feng, H.*, Bao, S.*, Rahman, M.,A., Weyn-Vanhentenryck, S.M., Khan, A., Wong, J., Shah, A., Flynn, E.D., Krainer, A.R., Zhang, C., 2019. Modeling RNA-binding protein specificity in vivo by precisely registering protein-RNA crosslink sites. Mol Cell. 74:1189-1204.E6.
3. Bao, S., Moakley, D.,F., Zhang, C., 2019. The splicing code goes deep. Cell, 176:414-416 (Leading Edge Preview).
4. Ustianenko, D.*, Chiu, H.-S.*, Treiber, T.*, Weyn-Vanhentenryck, S.M., Treiber, N., Meister, G., Sumazin, P. †, Zhang, C. † 2018. LIN28 selectively modulates a subclass of let-7 microRNAs. Mol. Cell. 71: 271-283.e5 (cover story).
5. Weyn-Vanhentenryck, S.M.*, Feng, H.*, Ustianenko, D., Duffié, R., Yan, Q., Jacko, M., Martinez, J.C., Goodwin, M., Zhang, X., Hengst, U., Lomvardas, S., Swanson, M.S., Zhang, C. 2018. Precise temporal regulation of alternative splicing during neural development. Nat Commun, 9:2189.
6. Jacko, M., Weyn-Vanhentenryck, S.M., Smerdon, J.W., Yan, R., Feng, H., Williams, D.J., Pai, J., Xu, K., Wichterle, H. †, Zhang, C.† 2018. Rbfox splicing factors promote neuronal maturation and axon initial segment assembly. Neuron, 97: 853-868.e6 (issue highlight).
7. Ustianenko, D., Weyn-Vanhentenryck, S.M., Zhang, C. 2017. Microexons: discovery, regulation, and function. WIREs RNA. e1418. doi: 10.1002/wrna.1418 (review).
8. Shah, A., Qian, Y., Weyn-Vanhentenryck, S.M., Zhang, C. 2017. CLIP Tool Kit (CTK): a flexible and robust pipeline to analyze CLIP sequencing data. Bioinformatics, 33:566-567. DOI: 10.1093/bioinformatics/btw653.
9. Feng, H., Zhang, X., Zhang, C. , 2015. mRIN for direct assessment of genome-wide and gene-specific mRNA integrity from large-scale RNA-sequencing data. Nat Comm. 6:7816 (highlighted by Nat Meth, 12:910).
10. Yan, Q.* , Weyn-Vanhentenrycka, S.M.*,Wu, J., Sloan, S.A., Zhang, Y., Chen, K., Wu, J.-Q., Barres, B.A.† , Zhang, C.† 2015. Systematic discovery of regulated and conserved alternative exons in the mammalian brain reveals NMD modulating chromatin regulators. Proc. Nat. Acad. Sci. USA. 112:3445-3450.
1. A Ph.D. degree in Computational or Systems Biology, Bioinformatics, Computer Sciences, or related fields
2. A genuine interest in solving complex biological problems using quantitative approaches
3. Strong background in statistical modeling and machine learning; experience in genomic analysis using deep neural networks is a plus.
4. Solid programing skills (eg., C/C++ and/or python/perl)
5. Extensive experience in handling large-scale genomic data. Experience in deep sequencing data analysis is a plus.
6. Highly motivated and ability to work independently as well as to collaborate in a team setting
7. Excellent written and verbal communication skills
8. A minimal of one first-author paper published in related peer-reviewed journals
Competitive salaries and full benefits will be provided.
Applicants should send a curriculum vitae and names and contacts of three references by email to:
Dr. Chaolin Zhang
630 W 168th Street
P&S Building, Room 4-448
New York NY 10032
Columbia University is an Equal Opportunity and Affirmative Action Employer. Hiring is contingent upon eligibility to work in the United States. Women and minorities are encouraged to apply.
Ph.D. degree or equivalent