Job Title – Computational Biologist, Stanley Center
Location – Broad Institute, Cambridge MA
The Broad Institute’s Stanley Center for Psychiatric Research is working to identify the cellular and molecular pathways disrupted in brain disorders such as schizophrenia and autism, by utilizing advances in genetics and genomics pioneered at the Broad Institute. We are developing and applying tools to understand how implicated genes act in neurons and circuits. We use large scale, unbiased, systematic approaches in collaborative multidisciplinary research teams.
This position in the Stanley Center for Psychiatric Research involves computational analysis of functional genomics datasets with a strong emphasis on RNA-Seq. This person will develop new computational methods, apply existing computational methods, interpret results within a biological context, and integrate best RNA-seq analysis practices from groups at Broad and around the world. This researcher will work in close collaboration with laboratory scientists on analysis of a wide range of RNA-Seq projects with an emphasis on single cell approaches. The scope will include integration with other Stanley Center and external genomics datasets as well as occasional gene-specific inquiries for Stanley Center researchers. The ability to understand molecular-biological methods and relate them to read-level properties of RNA-Seq data will be important for contributing to methods development and developing companion analysis methods for new molecular-biological methods. The role will often involve rapid prototyping in support of a dynamic, fast-moving experimental program; it is focused on molecular biology applications relevant to investigation of brain function and psychiatric illness. The job entails close collaborations with multiple Broad and Broad-affiliated groups.
- Develop, apply, document, and maintain computational tools, both for own use and to support analysis by biologist colleagues without formal computational training. Critically evaluate computational solutions, finding and addressing failure modes on one’s own.
- Develop customized computational solutions supporting new kinds of RNA-Seq experiments, enabling the rapid, responsive use of sequencing in technology development. Understand the analytical needs of new experiments, working closely with molecular biologists. Conceive analytical/computational solutions that address the needs of such projects.
- Analyze large RNA-Seq datasets to profile and annotate transcriptomes from brain and neuronal cell populations, and provide analyses in formats accessible to a research community of biologists and geneticists.
- Follow relevant scientific literature to ensure use of optimal methods and understand emerging practices across the field.
- Contribute to reports and papers for presentation and publication and present at scientific conferences, as appropriate.
- Regularly attend and present results at Stanley Center related team meetings to share results, plan projects and experiments.
- Work with other Broad computational biologists experienced with RNA-Seq, including those in the Regev group, to learn, discuss, and integrate the best solution for each experiment or project.
- Ph.D. degree in Computer Science, Engineering, Physics, Math, Bioinformatics, Biology or other relevant scientific discipline or equivalent experience is required.
- Must have demonstrated experience designing computational methods and tools, including prior experience with algorithms relevant to computational biology
- Experience with and thorough understanding of statistical analysis is required
- Familiarity with next-generation sequence data analysis tools, ideally for Illumina; in particular, experience with human or mammalian genome sequence data is preferred
- Familiarity with a range of sequence alignment tools, ideally those used for RNA-Seq (TopHat, STAR, RSEM, etc.)
- Must have demonstrated proficiency with several of the following technologies: Perl, Python, Java, Matlab, R, C, C++, Unix
- Basic understanding of molecular biology and next generation DNA sequencing
- Familiarity with single cell data analysis is preferred
- Ability to work independently as well as part of a team in a fast-paced environment, while making necessary connections with experts in various computational analysis groups
- Self-starter, highly motivated
- Excellent communication skills
- Excellent organization and time management skills
EOE / Minorities / Females / Protected Veterans / Disabilities