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Tier-1 employer, metro location, and mid-level ML experience range increase applicant competition.
Highly specialized digital pathology and spatial biology expertise limits transferable candidates across industries.
Requires advanced degree, specific computational pathology expertise, and mandatory ML/ML framework skills.
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Develop and apply image analysis, computer vision, and machine learning methods for digital pathology datasets, including whole-slide imaging and spatial omics.
Build reproducible and scalable workflows using Python and ML/DL frameworks (PyTorch, TensorFlow) for tissue segmentation, cell phenotyping, feature extraction, and spatial analysis.
Collaborate with pathologists and translational teams to generate and interpret image-derived biomarkers; support validation, benchmarking, and deployment of computational imaging pipelines in cloud or HPC environments.
PhD or MS in Computer Science, Biomedical Engineering, Computational Biology, Bioinformatics, Electrical Engineering, Applied Mathematics, or related quantitative discipline.
Strong background in computer vision, image analysis, and machine learning/deep learning with biomedical imaging data (pathology preferred).
Proficiency in Python and common ML frameworks such as TensorFlow or PyTorch.
Work Experience Required: Preferred 3+ years in computational imaging, digital pathology, or medical image analysis.
Experienced in building and deploying advanced computer vision and deep learning algorithms specifically in digital pathology or spatial biology.
Proficient in designing scalable, reproducible image analysis pipelines integrating whole-slide imaging and multiplex spatial datasets.
Capable of collaborating effectively with interdisciplinary teams including pathologists, translational scientists, and software engineers in scientific research settings.