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Computational Scientist I - Digital Pathology Image Analysis
Broad Institute of MIT and Harvard
Cambridge, USA
Full-time
Senior · 4+ years experience
Onsite
USD 88000-124000 yearly / year
Discovered 1 weeks ago
QuPathGroovyPythonRPyTorchTensorFlow
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Description & Requirements
- The Broad Institute of MIT and Harvard is the world's leading biomedical research institute dedicated to the bold mission of using the full power of genomics to transform the understanding and treatment of disease. We seek to describe all of the molecular components of life and their connections; discover the molecular basis of major human diseases; develop effective new approaches to diagnostics and therapeutics; and disseminate discoveries, tools, methods, and data openly to the entire scientific community.
- We are seeking a highly motivated and skilled Computational Scientist I to join our Surfaceome Target Discovery team and the Getz lab. In this role, you will play a critical part in accelerating our target validation pipeline by developing, optimizing, and deploying quantitative image analysis workflows. You will work closely with the Broad Cancer Cell Line Factory, the Dana-Farber Cancer Institute (DFCI) Pathology Core, and the Sellers lab in a highly collaborative and multidisciplinary research environment.
- The ideal candidate will have extensive hands-on experience handling whole-slide tissue images, specifically single plex/multiplex Immunohistochemistry (IHC) and multiplex Immunofluorescence (mIF) panels. You will translate target expression patterns, immune microenvironment, and spatial biology into robust, reproducible quantitative metrics to drive surface target validation in patient tumor and normal adult tissue microarrays.
Key Responsibilities
- Design, develop, and optimize machine learning and AI-based methods for digital pathology applications, including image segmentation, classification, spatial analysis, and predictive modeling using QuPath and other imaging platforms.
- Build and maintain scalable pipelines for preprocessing, quality control, and analysis of tissue microarray (TMA) and whole-slide imaging datasets generated from high-plex multiplex immunofluorescence (mIF) and chromogenic IHC assays.
- Develop and execute validation strategies for imaging workflows and classifiers through benchmarking, performance assessment, and external dataset validation to ensure analytical robustness and reproducibility.
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