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The quantitative analysis of cell and tissue shape – morphometrics – is pivotal for our understanding of human disease and our quest for cures. In recent years, Fred Hutch researchers have generated accelerating terabyte and pentabyte quantities of 3D and 4D imaging data. This has created a need for expertise in image analysis – encompassing modeling, statistical inference, and toolkit development. Accordingly, we are seeking a highly motivated individual with experience in morphometrics and image analysis to support research at the Fred Hutchinson Cancer Research Center (Fred Hutch).
The Morphometry and Image Analyst will collaborate with individual researchers from the Division of Basic Sciences and other Fred Hutch scientific divisions to develop multi-pronged integrative approaches to quantitatively understanding patterns in biological images, including ultra-resolution, 3D and 4D microscopy images. The analyst will interface with staff from a number of Fred Hutch Shared Resources (cores), specifically Scientific Imaging, Bioinformatics, and Scientific Computing, and will report to the Director of the Genomics and Bioinformatics Shared Resource.
This is a multi-faceted job that involves the following components:
The ideal candidate will have extensive experience in developing and utilizing computational approaches for quantitative image analysis and morphometry. Typically, the candidate will have developed these skills through research for an advanced degree in a biophysical, biomedical or bioengineering discipline and have at least 3 years of hands-on experience. They will have a fundamental understanding of microscopic techniques, statistics and mathematical modeling. Strong programing skills in MatLab, Python and/or R are essential. Experience with one or more common image analysis software packages is desirable. Additional critical skills include the ability to manage multiple ongoing projects, communicate efficiently and effectively with scientists and technical staff, maintain detailed documentation, and function independently in a dynamic research setting.