Scientist I – Computational analysis of cell types in human brain using single nucleus RNA-sequencin
February 8, 2018
Full Time - Experienced
Academic / Research
The Allen Institute for Brain Science, located in Seattle, Washington, has an ambitious, multi-disciplinary effort to reverse engineer complex neural circuits by understanding their diverse cellular parts. A key component of this effort is to define and characterize neural cell types based on shared features. Single nucleus RNA-seq has revolutionized this field by allowing a high-throughput analysis of cell types and classification based on their molecular profiles, which are highly predictive of specific aspects of cellular function and relation to disease. We are generating unique, large-scale (up to millions of cells) single nucleus RNA-seq datasets across human brain regions, and we seek to hire a Scientist to help develop novel computational tools to analyze and interpret these data. This position will collaborate with an enthusiastic and dynamic team of quantitative and experimental scientists to build a quantitative census of cell types in human brain and to look for signatures of neurological disease and uniquely human features.
ESSENTIAL DUTIES & RESPONSIBILITIES
Develop novel computational tools to rigorously define and characterize cell types.
Compare cell types across brain regions and species and relate expression to other cell type features.
Participate in team efforts to develop highly scalable RNA-sequencing analyses.
Work with experimental scientists to design experiments to validate computational predictions of neurobiological function.
Participate in a highly interactive and multidisciplinary environment.
Publish and present findings in peer-reviewed journals and scientific conferences.
PhD degree in neuroscience, bioinformatics, genetics, physics, applied mathematics, engineering, or related field.
Familiarity with RNA-sequencing data.
Fluency programming in R and/or Python.
Fluency programming in R.
2+ years of experience analyzing RNA-seq data, preferably at the single cell level.