Students
Graduate Student RAship: Computer Vision & Pathology Foundation Models
Posted 08/27/2026 (starting Fall Qtr 2026)
Professor Sylvia Plevritis’ laboratory at Stanford University is seeking a Stanford PhD student for a graduate Research Assistantship (RAship) focused on computer vision, pathology foundation models, and vision-language models for whole-slide image (WSI) analysis. The RAship will focus on developing and evaluating next-generation AI models for computational pathology, with opportunities to contribute to methodological advances and clinically relevant cancer applications.
Research Focus:
- Pathology foundation models: Develop and benchmark models across downstream pathology and clinical tasks.
- Vision-language models: Integrate whole-slide images with pathology reports to enable multimodal analysis.
- Model adaptation: Fine-tune and optimize foundation models for specific datasets, tasks, and pathology workflows.
Required Qualifications:
- Strong background in computer vision and deep learning
- Proficiency with PyTorch
- Experience with transformers and/or foundation models
- Strong programming and communication skills
- Active enrollment in a Stanford PhD program
Preferred Qualifications:
- Experience with vision-language or multimodal models
- Experience with histopathology or medical imaging
- Interest in AI applications in cancer and clinical medicine
The RA will work closely with computational scientists and clinical researchers in an interdisciplinary environment in the James H. Clark Center. To apply, please email your CV/resume and a brief description of relevant research experience, accomplishments, and research interests to Anna Kaur at kauram@stanford.edu with the subject line: “RAship in Pathology Whole Slide Image Analysis.”
Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.