AI Innovation Postdoctoral Fellow in Digital Pathology

Develop and apply AI 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. Collaborate with pathologists and translational teams to interpret image-derived biomarkers and biological findings. Contribute to scientific publications and present results at internal and external scientific conferences.

We are excited to invite applications for the Novartis Biomedical Research Postdoctoral Fellowship Program, a unique training opportunity designed for exceptional early-career scientists eager to tackle some of the most challenging problems in biomedical research and drug discovery. As a Postdoctoral Research Fellow, you will join the AI & Innovation team within Oncology Data Science at Novartis in Basel and pursue an innovative research project at the forefront of biomedical science and drug discovery. You will work alongside leading scientists in a highly collaborative, multidisciplinary environment while gaining exposure to the broader ecosystem that translates scientific discovery into medicines. The Novartis Biomedical Research Postdoctoral Fellowship Program is designed to develop the next generation of scientific leaders and power the future of medicine through rigorous research, and immersive learning experiences, including the implementation of AI tools in biomedical research. At Novartis, our purpose is to reimagine medicine to improve and extend people’s lives. Through this program, you will grow as a scientist and future leader while contributing to discoveries that may ultimately benefit patients worldwide.

• PhD (or equivalent doctoral degree) in а relevant scientific discipline, completed prior to the fellowship start date. The program is intended for scientists immediately following their PhD training (graduated in 2026).

• Demonstrated record of scientific achievement (publications, presentations, patents, or equivalent)

• Strong commitment to learning, innovation, and professional development

• Experience analyzing imaging data (e.g. H&E;, IHC, multiplex IF, spatial transcriptomics).

• 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.