Columbia University's Department of Systems Biology is seeking multiple qualified candidates for research positions of Postdoctoral Research Scientists (Computational) in Dr. Andrea Califano's laboratory (http://califano.c2b2.columbia.edu/). Candidates should have outstanding scientific and doctoral credentials, including first-author publications in journals relevant to the field of systems biology, and conference presentations. Candidates possessing a strong computational background combined with a good understanding of molecular biology will be especially well suited to fill this position, including background in machine learning, physics, mathematics, and related sciences. In addition, the optimal candidate will have significant experience and publications in network-based biology, including the reverse engineering and/or use of regulatory/signaling networks to elucidate the molecular determinants of specific cellular phenotypes and associated mechanisms for follow-up experimental validation.
Integration of quantitative analysis, high-throughput experimentation, and technology development is the hallmark of Systems Biology at Columbia. As such, the Postdoctoral Research Scientists will work collaboratively on multidisciplinary teams to develop or improve algorithms for the analysis of cell regulatory and signaling networks; use models to predict how the genomic and epigenomic diversity affect physiologic or pathologic phenotypes; and develop new methods and technologies for elucidating biological mechanisms at the systems level. The Califano lab has an outstanding record of placement for its former members ranging from academic position, including department chairs, to leading positions in industry, from group leader to chief scientific officer.
PhD in one of the following disciplines: computational biology, physics, computer science, mathematics, or related quantitative science is required.
A proven first-author publication track record is required to be considered for this position.
Knowledge of standard computer languages, including R, Java, PERL, and C/C++.
Familiarity with processing large genomic data sets and with a variety of reverse engineering techniques, including optimization-based, statistical, and integrative approaches.
Familiarity with network biology algorithms, including both network reverse engineering and analysis, as well as with the underlying biological knowledge related to transcriptional and post-translational interactions is critical to be considered for this position, as is in-depth knowledge of the foundations of machine learning and probability theory.
Excellent communication and writing skills.
Strong project management skills and demonstrated mentoring skills with students.
Please upload your cover letter and CV.
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