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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 •

Last updated on Oct 17, 2023

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