Perform tissue segmentation, cell phenotyping, feature extraction, and spatial analysis using state-of-the-art computational approaches.
Collaborate with pathologists and translational teams to interpret image-derived biomarkers and biological findings.
Contribute to evaluation, validation, and benchmarking of image analysis algorithms and workflows.
Support deployment and operationalization of computational imaging pipelines in cloud or high-performance computing environments.
Stay current with advances in computational pathology, AI/ML, and spatial biology technologies.
Win – What we expect of you
Basic Qualifications
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.
Demonstrated experience working with biomedical imaging data (pathology preferred).
Proven track record in developing, validating, and deploying advanced computer vision and deep learning algorithms, including contributions to released products.
Proficiency in Python and common ML frameworks (e.g., TensorFlow, PyTorch).
Deep understanding of image analysis methods such as segmentation, object detection, and classification, ideally within digital pathology (e.g., histopathology, tissue imaging) and/or spatial omics.
Familiarity with large-scale data management and cloud-based computational environments.
Strong communication and interpersonal skills
Preferred Qualifications
3+ years of experience in computational imaging, digital pathology, or medical image analysis.
Strong programming skills in Python and experience with scientific computing libraries.
Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow.
Experience analyzing pathology imaging modalities including H&E, IHC, multiplex IF, or spatial transcriptomics data.
Familiarity with whole-slide imaging tools and digital pathology platforms such as QuPath, HALO, Visiopharm, or OpenSlide.
Understanding of machine learning methods for segmentation, classification, detection, and representation learning.
Strong communication and collaboration skills in multidisciplinary scientific environments.
Track record of scientific publications, conference presentations, or applied translational research contributions is preferred.
Thrive – What you can expect of us
As we work to develop treatments that take care of others, we also work to care for our teammates’ professional growth and well-being:
A collaborative and diverse global team environment
Opportunities to work with cutting-edge technologies in biomedical research
Training and mentoring to support your career path
Flexibility in work arrangements and work-life balance
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