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Computational Biologist, Viswanathan Lab
Dana-Farber Cancer Institute in the Boston, Massachusetts
 
 
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Date Posted November 13, 2019
Category
Science-Computer Sciences
Employment Type Full-time
Application Deadline Open until filled
 
 
 

Overview

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute brings together world renowned clinicians, innovative researchers and dedicated professionals, allies in the common mission of conquering cancer, HIV/AIDS and related diseases. Combining extremely talented people with the best technologies in a genuinely positive environment, we provide compassionate and comprehensive care to patients of all ages; we conduct research that advances treatment; we educate tomorrow's physician/researchers; we reach out to underserved members of our community; and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

The Viswanathan lab focuses on understanding the molecular mechanisms that drive genitourinary malignancies, particularly prostate and kidney cancers. We employ a variety of cutting-edge genomic and functional genetic technologies to understand the drivers of these cancers with the goal of identifying new therapeutic targets.

The Computational Biologist will contribute to several exciting projects in the lab, related to understanding the role of noncoding variants and post-transcriptional changes in driving cancer. The Computational Biologist will interact and collaborate with other wet and dry-lab scientists at both the Dana-Farber Cancer Institute and the Broad Institute of Harvard and MIT.

 

 

Candidates with a BS/MS degree and future plans to attend MD, PhD, or MD/PhD programs after a period in the lab are strongly encouraged to apply, and career development to this end will be enthusiastically supported.

 

Responsibilities

The successful candidate will have a strong interest in analysis of large-scale genetic and transcriptomic datasets with the ultimate goal of discovering novel cancer drivers. The position will involve developing methods and pipelines for analysis of short- and long-read whole genome sequencing, transcriptome sequencing, ChIP-Seq, and CLiP-Seq data. Prior experience with one or more of these data types is strongly preferred.

 

The successful candidate will be highly motivated and collaborative with strong communication skills, and should have the desire to interface both with other computational/quantitative biologists and functional biologists. Applicants should have proficiency and substantial programming experience in R and/or Python Familiarity or proficiency with Matlab, Java C/C++, Perl or other languages as used in biomedical research/biomedical informatics will be a plus.

 

 

Specific duties will include:

  • Ability to deploy existing programs and algorithms for analysis of next-generation sequencing data in support of multiple projects.

  • Development and maintenance of pipelines for analysis of next-generation sequencing data

  • Maintenance/stewardship of large-scale datasets generated by the laboratory.

  • Genomic/transcriptomic discovery of novel somatic alterations that drive cancer and the potential for developing independent projects in this area

  • Development and deployment of analytic methods for analysis of data from linked-read / long-read sequencing platforms.

Qualifications

  • BS/MS/PhD in computer science or related quantitative discipline

  • Strong programming experience in R and/or Python and familiarity with Matlab, Java C/C++, Perl

  • Experience with data visualization of information gleaned from large-scale datasets.

  • Prior experience with analysis of next-generation sequencing data is strongly preferred.

  • Experience with pipeline management (e.g. Snakemake) preferred.

  • Excellent interpersonal skills with a collaborative attitude

  • Detail-oriented with excellent organizational skills.

Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other groups as protected by law.

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