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(Senior) Scientist, Computational Biology (RNA Therapeutics)

Employer
Dark Horse Talent
Location
Cambridge, Massachusetts, US
Salary
Competitive
Closing date
Sep 29, 2022

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Sector
Academic / Research
Field
Informatics / GIS
Discipline
Biology
Salary Type
Salary
Employment Type
Full time
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We are building a world-class team and looking for passionate researchers committed to improving human health through advances in RNA biology/therapeutics.

This is an opportunity to further develop our computational platform by working closely with experimental scientists to analyze, interpret, and visualize data from novel assays related to RNA biology and compound treatment.

Opportunity to:
  • Assist in maintaining/developing RNA-seq analysis pipelines to identify differentially expressed genes, quantify transcript levels, and characterize alternative splicing events.
  • Employ and develop methods to characterize the binding preferences and functions of RNA-binding proteins (e.g., analyzing CLIP data).
  • Participate in the development of novel assays and downstream analysis to evaluate the biological effects of small-molecule binding to RNA structures.
  • Participate in building a scalable, cloud-based computing infrastructure that automates analyses and generates reproducible results in a timely manner.
  • Follow best practices for collaborative software development, such as using repositories for version control and participating in code reviews.
  • Contribute to a culture of effective collaboration between experimental and computational scientists.
  • Survey the scientific literature to stay informed of the latest tools and databases.


We are looking for:
  • Ph.D. in Computational Biology, Bioinformatics, Computer Science, RNA Biology or related discipline.
  • An understanding of RNA biology and experience working with data types relevant for studying RNA.
  • Ability to access and mine large sequencing datasets to address biological questions and generate hypotheses. Examples of relevant data types include RNA-seq, CLIP-seq, ChIP-seq, ribosome profiling, and SHAPE-MaP.
  • An understanding of statistical methods relevant to the life sciences.
  • Ability to communicate methodologies and results effectively with colleagues, including bench scientists, research management, and project team leaders.


Nice to have :
  • Experience mining publicly available datasets related to oncology.
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