Broad Institute

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.
• Collaborate with pathologists and scientists to perform image quality assessment, evaluate classifier accuracy, and identify imaging artifacts, staining variability, segmentation errors, and tissue integrity issues prior to downstream analysis.
• Partner with pathologists, assay biologists, translational researchers, and/or data scientists to translate biological and clinical questions into quantitative imaging metrics and actionable analytical outputs.
• Coordinate imaging analyses and deliverables across cross-functional teams to support project goals, timelines, and study milestones.
• Maintain well-documented, reproducible workflows, including SOP-compliant analysis records, QC documentation, version-controlled code repositories, and organized large-scale imaging datasets.

Qualified Candidates Should Have:

• Master’s or Ph.D. degree in Biomedical Engineering, Bioinformatics, Computer Science, Computational Biology, or a related quantitative life sciences discipline with expertise in digital pathology and image analysis.
  • Candidates with a Master’s degree should have at least 4+ years of relevant industry or academic research experience.
• Demonstrated hands-on experience with QuPath, including cell detection, tissue microenvironment analysis, classifier development, and custom Groovy scripting for workflow automation and scalable image analysis.
• Strong understanding of tissue-based imaging assays and spatial biology technologies, including H&E, chromogenic IHC, mIF, and related imaging platforms such as Akoya PhenoImager/Opal, COMET, or equivalent systems.
• Proficiency in Python, R, and/or Groovy for image analysis, spatial data analysis, statistical modeling, and workflow development; experience with deep learning frameworks (e.g., PyTorch or TensorFlow) for tissue segmentation and classification is highly desirable.
• Experience developing reproducible and well-documented computational workflows, including version-controlled code management and structured data organization practices.
• Strong analytical, problem-solving, and communication skills, with the ability to work effectively in multidisciplinary research environments.

Preferred Qualifications:

• Experience with commercial digital pathology platforms such as Indica Labs HALO/HALO AI, Visiopharm, or related image analysis software.
• Familiarity with cloud-based computing environments (AWS, Google Cloud Platform) and containerized workflow development using Docker or similar technologies.
• Experience supporting translational oncology, immuno-oncology, or biomarker discovery programs in academic, biotech, or pharmaceutical research settings.
• Demonstrated scientific contributions through publications, collaborative projects, or presentations involving computational pathology, spatial biology, or quantitative imaging analyses.