Scientist I - Spatial Transcriptomics Analysis

Scientist I - Spatial Transcriptomics Analysis - Imaging

The mission of the Allen Institute is to unlock the complexities of bioscience and advance our knowledge to improve human health. Using an open science, multi-scale, team-oriented approach, the Allen Institute focuses on accelerating foundational research, developing standards and models, and cultivating new ideas to make a broad, transformational impact on science.

The mission of the Allen Institute for Brain Science is to accelerate the understanding of how the human brain works in health and disease. Using a big science approach, we generate useful public resources, drive technological and analytical advances, and discover fundamental brain properties through

The Allen Institute for Brain Science is pursuing in-depth mapping of molecular cell types in the brain and the Imaging Department is recruiting a Scientist to analyze spatial transcriptomics data. The Imaging Department is a group of ~20 scientists and research associates using widefield, confocal, 2-photon, and light-sheet microscopes to support many projects. As a central focus of the department, spatial transcriptomics methods are now generating data at high rates and this Scientist will help translate this data into novel biological insight about the organization of cell types in the brain.

As part of this interdisciplinary team, you will develop analysis software to integrate gene expression at the single-cell level, molecular cell type identity and anatomical organization across multiple structures in the brain. To accomplish this, you will benchmark existing spatial analysis methods, create novel approaches of synthesizing spatial gene expression data from multiple experiments and develop methods for spatial parcellation of brain tissue based on cell types and gene expression. Initial work will focus on the mouse thalamus and subsequent projects will expand to include other areas of the brain and other species.

We anticipate two areas of experience will be useful for fulfilling this role: experience analyzing spatial transcriptomics data (MERFISH, in-situ sequencing, seqFISH, etc.), or experience with quantitative spatial analysis from computational biology, spatial statistics, image processing or related fields combined with a willingness to explore a new type of biological data.

The Allen Institute believes that team science significantly benefits from the participation of diverse voices, experiences and backgrounds. High-quality science can only be produced when it includes different perspectives. We are committed to increasing diversity across every team and encourage people from all backgrounds to apply for this role.

Essential Functions

* Research and benchmark existing spatial analysis methods for integrating spatial transcriptomics data in anatomical context
* Develop new spatial analysis methods for registering and merging data across experiments and anatomical structures to reveal novel parcellations and the spatial organization of gene expression
* Provide actionable feedback and interaction in a scientific team environment, including regular discussions on data collection, QC and pipeline engineering
* Effectively communicate findings across the institute as well as to the greater scientific community in peer-reviewed journals, scientific conferences, and web documentation
* Develop scientific software tools with clear documentation and good source code management

Note: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. This description reflects managements assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned.

Required Education and Experience

* Ph.D. in computational biology, neuroscience, physics, applied mathematics, or related field, or equivalent combination of degree and experience
* Experience in spatial statistics, scRNASeq analysis, computational anatomy or related fields

Preferred Education and Experience

* Experience with analysis of spatial transcriptomics data in the brain
* Understanding of transcriptomically-defined neuronal cell types from scRNASeq data
* Experience with modern image processing, especially machine learning for large microscopy datasets
* 3-5 years experience in scientific computing in Python and Linux/UNIX environment
* Contributions to open-source software projects, including source code management and documentation
* Strong written and verbal communication skills

Physical Demands

* Fine motor movements in fingers/hands to operate computers and other office equipment; repetitive motion with lab equipment

Position Type/Expected Hours of Work

* This role is currently able to work remotely due to COVID-19 and our focus on employee safety. We are a Washington State employer, and remote work must be performed in Washington State. We continue to evaluate the safest options for our employees. As restrictions are lifted in relation to COVID-19, this role will return to work in a hybrid work environment.

Additional Eligibility Qualifications

* In keeping with our focus on employee safety, all employees must be up to date with vaccinations against COVID-19 as a condition of employment unless a medical or religious accommodation is approved. All employees will be required to keep their vaccination status up to date according to CDC guidance.

Additional Comments

* Please note, this opportunity offers relocation assistance
* Please note, this opportunity offers work visa sponsorship

It is the policy of the Allen Institute to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, the Allen Institute will provide reasonable accommodations for qualified individuals with disabilities.