AI Scientist – Image Analysis & Digital Pathology
Novartis
August 20, 2026
On-site
Cambridge, MA
Clinical Research and Development
Key responsibilities:
- Develop and apply AI/deep learning approaches to extract insights from digital pathology imaging datasets (e.g., H&E, IHC, mIF, spatial transcriptomics).
- Work with foundational models (pre-trained, self-supervised, multi-purpose, multi-modal) to advance generative AI applications in drug discovery.
- Support imaging biomarker development by analyzing pathology imaging data; 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.
- Perform tissue segmentation, cell phenotyping, feature extraction, and spatial analysis using state-of-the-art computational approaches.
- Stay current with computational pathology, AI/ML, and spatial biology advances; contribute to publications and present results at internal/external conferences.
Requirements:
- Ph.D. in Machine Learning, Computer Science, Applied Mathematics, Computational Biology, or related field; or MSc with 4+ years of relevant industry experience.
- Strong experience analyzing pathology imaging modalities including H&E, IHC, multiplex IF, and/or spatial transcriptomics.
- Understanding of ML methods for segmentation, classification, detection, and representation learning.
- Experience in one or more: generative AI, digital pathology foundation models, geometric deep learning, and/or multi-modal learning.
- Excellent programming skills and proficiency in deep learning frameworks such as PyTorch; openness to new tools/technologies.
- Strong communication/collaboration; ability to convey complex insights to cross-functional teams.
- Strong research skills, evidenced by publications and/or conference presentations.
Benefits:
- Relocation is offered for this role.
- Flexible and hybrid working options (where possible).
- Minimum of 14 weeks paid parental leave.
- Competitive benefits in kind (insurance plans, retirement plans, wellbeing resources, global recognition programs).
- Develop and apply AI/deep learning approaches to extract insights from digital pathology imaging datasets (e.g., H&E, IHC, mIF, spatial transcriptomics).
- Work with foundational models (pre-trained, self-supervised, multi-purpose, multi-modal) to advance generative AI applications in drug discovery.
- Support imaging biomarker development by analyzing pathology imaging data; 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.
- Perform tissue segmentation, cell phenotyping, feature extraction, and spatial analysis using state-of-the-art computational approaches.
- Stay current with computational pathology, AI/ML, and spatial biology advances; contribute to publications and present results at internal/external conferences.
Requirements:
- Ph.D. in Machine Learning, Computer Science, Applied Mathematics, Computational Biology, or related field; or MSc with 4+ years of relevant industry experience.
- Strong experience analyzing pathology imaging modalities including H&E, IHC, multiplex IF, and/or spatial transcriptomics.
- Understanding of ML methods for segmentation, classification, detection, and representation learning.
- Experience in one or more: generative AI, digital pathology foundation models, geometric deep learning, and/or multi-modal learning.
- Excellent programming skills and proficiency in deep learning frameworks such as PyTorch; openness to new tools/technologies.
- Strong communication/collaboration; ability to convey complex insights to cross-functional teams.
- Strong research skills, evidenced by publications and/or conference presentations.
Benefits:
- Relocation is offered for this role.
- Flexible and hybrid working options (where possible).
- Minimum of 14 weeks paid parental leave.
- Competitive benefits in kind (insurance plans, retirement plans, wellbeing resources, global recognition programs).