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:
We are looking for:
- 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.
Nice to have :
- 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.
- Experience mining publicly available datasets related to oncology.