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Faculty All Ranks (Tenure track and Tenured) (copy)

Employer
Columbia University Medical Center - DSB
Location
New York City, New York (US)
Salary
Salary commensurate with experience.
Closing date
Sep 24, 2021

Job Details

The Program for Mathematical Genomics in the Department of Systems Biology at Columbia University in New York City invites outstanding candidates to apply for tenure track/tenured faculty positions at the level of Assistant Professor or higher rank. This new program seeks individuals with exceptional accomplishments and abilities in broad areas of quantitative biology, including but not limited to systems biology (data-driven and model/theory-based), mathematical and theoretical biology, machine learning, functional genomics, dynamical systems modeling, molecular and single-cell biology, and methods development for the analysis and understanding of biological datasets and their associated phenomena. The successful candidate will join a friendly, highly collaborative faculty and will have access to superb resources. We seek faculty members with a strong commitment to research, mentoring, and teaching, fostering a climate that embraces both excellence and diversity.

Applicants must have:

A Ph.D. in any quantitative discipline.

Active research in quantitative approaches to biological problems.

Must demonstrate the ability to develop a creative independent research program.

Applicants should include a cover letter, curriculum vitae, and three-page summary/statement of current and proposed research. We also require three letters of recommendation. Apply here: pa334.peopleadmin.com/postings/7274.

Required documents may also be sent to pr2470@cumc.columbia.edu

All positions are open until October 15, 2021.

Columbia University is an Equal Opportunity Employer / Disability / Veteran

Company

Welcome to the Rabadan Lab at Columbia University!

The amount of high throughput data in biological and clinical systems—from next-generation sequencing experiments to electronic health records—is increasing dramatically, allowing for the development of a quantitative understanding of these complex systems. Our lab is an interdisciplinary team interested in developing mathematical and computational tools to extract useful biological information from large data sets.

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