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We are seeking a highly motivated analyst to process the in-house Optical Pooled Screens (OPS) (Feldman, et al 2019, Feldman, et al 2022, Funk, et al 2022) images and analyze the data for ongoing projects. The successful candidate would process and analyze the microscopy images for on-going scientific projects, as well as contribute to the code base of softwares being developed in-house. The analyst will need to work closely with both experimentalists and statisticians to ensure that data and results are communicated smoothly.


Principal Duties and Responsibilities
Under the supervision of project manager, investigate and apply the best-in-class algorithms to process raw microscopy image, extract tabular information from images, assess the quality of the data and carry out statistical analysis
Work closely with cross-functional teams to understand analytical needs, independently and collaboratively formulate analysis plan and implement corresponding analytical workflows, and actively participate in discussions with stakeholders for feedback
Code in Python for image processing and analysis tasks
Effectively present analysis results in a clear and concise manner to key stakeholders


Must-Have Qualifications
PhD in Bioinformatics, Computer Science, Machine Learning, Statistics or Computational Biology. Alternatively, you have a PhD in molecular biology, immunology, bioengineering, etc. combined with a very strong record of data analysis, supported by publication in this area.
3+ years experience (including any graduate school) developing or applying biomedical image processing tools and statistical analysis softwares of microscopy image data.
Comfortable with the statistical principles behind current best practices in high-throughput molecular data analysis.
Experience in the Python biomedical image analysis ecosystem, including scipy, scikit-image, imageio, xarray, scikit-learn.
Demonstrated ability to effectively communicate about complex bioinformatics problems to peers, users, and stakeholders.
Motivated to learn new biology, technology and analytical methods


Nice-to-Have Expertise
Familiarity with deep learning approaches for cell segmentation, feature extraction, and phenotypic analysis
Familiarity with high-dimensional multiplexed imaging data and associated data/image models
Familiarity with domain-specific image processing tools, such as cellpose, cellprofiler and stardist
Familiarity with workflow development tools, such as Snakemake or Redun
Analysis of large chemical/genetic screening datasets
Single-cell genomics and trajectory analysis


References:

(1) Feldman D, Funk L, Le A, Carlson RJ, Leiken MD, Tsai F, Soong B, Singh A, Blainey PC. Pooled genetic perturbation screens with image-based phenotypes. Nat Protoc. 2022 Feb;17(2):476-512. doi: 10.1038/s41596-021-00653-8. Epub 2022 Jan 12. PMID: 35022620; ProductID: Product9654597.

(2) Funk L, Su KC, Ly J, Feldman D, Singh A, Moodie B, Blainey PC, Cheeseman IM. The phenotypic landscape of essential human genes. Cell. 2022 Nov 23;185(24):4634-4653.e22. doi: 10.1016/j.cell.2022.10.017. Epub 2022 Nov 7. PMID: 36347254; ProductID: Product10482496.

(3) Feldman D, Singh A, Schmid-Burgk JL, Carlson RJ, Mezger A, Garrity AJ, Zhang F, Blainey PC. Optical Pooled Screens in Human Cells. Cell. 2019 Oct 17;179(3):787-799.e17. doi: 10.1016/j.cell.2019.09.016. PMID: 31626775; ProductID: Product6886477. •

Last updated on Oct 17, 2023

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