The Laboratory of Dr. Chaolin Zhang in the Department of Systems Biology at Columbia University Irving Medical Center (CUIMC) has multiple positions for Postdoctoral Research Scientist in AI/ML, Computational Biology and RNA Genomics to conduct NIH-funded research on mammalian RNA regulatory networks. The Zhang lab is searching 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 .

　　The postdocs 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 postdocs 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.

　　Minimum Qualifications:

　　- Ph.D. degree in Computational or Systems Biology, Bioinformatics, Computer Sciences, or related field.

　　- A genuine interest in solving complex biological problems using quantitative approaches.

　　- Strong background in statistical modeling and machine learning.

　　- Solid programing skills (eg., C/C++ and/or python/perl).

　　- Extensive experience in handling large-scale genomic data.

　　- Highly motivated and ability to work independently as well as to collaborate in a team setting.

　　- Excellent written and verbal communication skills.

　　- At least one first-author paper published in related peer-reviewed journals.

　　Preferred Qualifications:

　　- Experience in genomic analysis using deep neural networks is a plus.

　　- Experience in deep sequencing data analysis is a plus.

　　Columbia University is an Equal Opportunity Employer / Disability / Veteran

　　Pay Transparency Disclosure

　　The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.

　　Equal Opportunity Employer / Disability / Veteran

　　Columbia University is committed to the hiring of qualified local residents.

　　Minimum Salary: 31200.00Maximum Salary: 31200.00Salary Unit: Yearly