OHSU
Location
Portland, OR | United States
Job description
Department Overview:
The Cancer Early Detection Advanced Research (CEDAR) center is a collaborative institution within the OHSU Knight Cancer Institute. The Knight Cancer Institute, known as one of the pioneers in personalized cancer medicine, is an international leader in research and cancer treatment. At CEDAR our mission is to detect and stop lethal cancers at the earliest stage because early detection saves lives. This is an ambitious goal, one that requires novelty, creativity, and innovation. We are comprised of biologists, chemists, biomedical engineers, computational biologists, and clinicians conducting groundbreaking translational cancer research to help people maintain a high quality of life and reduce cancer mortality, to create a global early detection community, and to have a positive impact on the Oregon economy.
Our Commitment to Diversity: CEDAR is committed to increasing the diversity of the campus community. We are dedicated to promoting inclusion and multiculturalism by having outstanding researchers of diverse backgrounds work together on multiple projects. We encourage high-risk, high-reward research projects because defeating cancer requires out-of-the-box thinking and new perspectives. Our research is milestone driven to ensure that each project is fulfilling its stated goals.
The Knight Cancer Institute Cancer Early Detection Advanced Research Center (CEDAR) at Oregon Health & Science University seeks an experienced Computational Biologist Level 1 or 2 who will develop custom computational methods, including construction of analytic pipelines and implementation of sophisticated statistical techniques to analyze spatial imaging data relevant to cancer biology and early detection. The position will enjoy unique exposure and access to single cell multiplex imaging data sets and single-cell transcriptomic data as a member of CEDAR¿s collaborative spatial imaging team, but no prior experience in image analysis specifically is required for this position. The ideal candidate will have experience with analysis of `omic data (genomic, transcriptomic, proteomic etc.), particularly for target or biomarker discovery, experience in implementing statistical methods as appropriate for dataset treatment (e.g. quality inspection, normalization, differential feature detection, etc.), and experience in developing pipelines to support analytics as described to additionally facilitate improved data management, and to generate complete workflows that are reproducible and scalable. Experience with or knowledge regarding single-cell data and/or machine learning would be a bonus but is NOT required and should not be a deterrence from applying. You will be part of multidisciplinary project-based teams developing novel approaches for the early detection of cancer. Duties include requirements gathering, data management, pipeline building, shared tool development, custom analysis with data modeling and machine learning, and contributions to manuscripts and grant applications, all under the supervision of senior level computational biologists.
The ideal candidate will have experience with analysis of ‘omic data (genomic, transcriptomic, proteomic etc.), particularly for target or biomarker discovery, experience in implementing statistical methods as appropriate for dataset treatment (e.g. quality inspection, normalization, differential feature detection, etc.), and experience in developing pipelines to support analytics as described to additionally facilitate improved data management, and to generate complete workflows that are reproducible and scalable. Experience with or knowledge regarding single-cell data and/or machine learning would be a bonus but is NOT required and should not be a deterrence from applying.
You will be part of multidisciplinary project-based teams developing novel approaches for the early detection of cancer. Duties include requirements gathering, data management, pipeline building, shared tool development, custom analysis with data modeling and machine learning, and contributions to manuscripts and grant applications, all under the supervision of senior level computational biologists.
Every Knight Cancer employee is expected to embody our guiding principles:
Data Organization & Management
Data Analysis
Technical Writing/Communication
Career Development
Protected Work Tme
Significant experience with analysis of ‘omic data (genomic, transcriptomic, proteomic etc.), particularly for target or biomarker discovery
Experience in implementing statistical methods as appropriate for dataset treatment (e.g. quality inspection, normalization, differential feature detection, etc..
Experience in developing pipelines to support analytics as described to additionally facilitate improved data management, and to generate complete workflows that are reproducible and scalable
High-level competency in an appropriate programming language (R or Python)
An understanding of a variety of sophisticated statistical methods for data treatment
Usage of structured engineering methods for specifying, designing, and implementing systems and pipelines in high performance computing environments
Ability to prioritize multiple tasks at one time
Good communication, analytical and organizational skills, both written and verbal
Experience with or knowledge regarding single-cell data (e.g. from single cell RNA sequencing) would be a bonus but is NOT required and should not be a deterrence from applying
Experience with machine learning a bonus but not required
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