AI Scientist – Image Analysis & Digital Pathology

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, including pre-trained, self-supervised, multi-purpose, and multi-modal models to advance generative AI applications in drug discovery. Support imaging biomarker development efforts by analyzing pathology imaging data and 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 advances in computational pathology, AI/ML, and spatial biology technologies; contribute to scientific publications; and present results at internal and external scientific conferences.

The Oncology Data Science team in Biomedical Research at Novartis works at the intersection of oncology drug discovery, computational biology, AI/ML, and data engineering. We are seeking an enthusiastic AI/ML scientist with deep expertise in digital pathology and a strong curiosity for translational research to join the AI & Innovation Team.

• Ph.D. in Machine Learning, Computer Science, Applied Mathematics, Computational Biology or related field. Candidates with MSc in Machine Learning, Computer Science, Applied Mathematics, Computational Biology or related field, with 4+ years of relevant industry experience will also be considered.

• Strong experience analyzing pathology imaging modalities including H&E;, IHC, multiplex IF, and / or spatial transcriptomics data.

• Understanding of machine learning methods for segmentation, classification, detection, and representation learning.

• Experience in one or more of the following areas: 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, with openness to learning new tools and technologies.

• Strong communication and collaboration skills with the ability to communicate complex data insights and recommendations to cross-functional teams.

• Strong research skills, evidenced by publications in leading scientific journals and / or conference presentations.