Position overview Salary range: The salary range for this position is $66,737-$80,034. The posted UC salary scales set the minimum pay determined by experience level. See: Postdoc Scholar Salary Scale Effective 10/1/26: The salary range for this position is $71,491-$85,736. See: Postdoc Scholar Salary Scale Application Window Open date: July 1, 2026 Next review date: Tuesday, Sep 1, 2026 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee. Final date: Wednesday, Jun 30, 2027 at 11:59pm (Pacific Time) Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled. Position description The OctoPath Lab led by Prof. Lipkova, is seeking applications from exceptional postdoctoral research
fellows interested in developing deep learning and computational methods for pathology image analysis, multimodal data integration, and other medical modalities (e.g., proteomics, spatial transcriptomics, genomics, radiology). Postdoctoral applicants should have a PhD, a solid publication record and strong professional references. The ideal candidate should have a significant mathematical and computational background
to learn and develop new AI methods. While demonstrated experience in computer vision and deep learning for pathology or other biomedical image analysis is desirable, applicants with a strong machine learning background and an interest in transitioning to biomedical data science are also encouraged to apply. Initial appointments are for two-years and renewal is based on performance and available support. Environment
and Opportunities The lab focuses on developing AI methods to improve patient diagnosis, prognosis, treatment response prediction, and treatment optimization. We are particularly interested in a) developing novel AI methods for analyzing pathology data, b) multimodal data fusion strategies, c) development of computational methods for rare diseases and new modalities (e.g., multiphoton microscopy), and d) translating
AI solutions into clinical practice through the development of AI-based embedded devices. We are committed to applying these techniques to pressing clinical needs in medicine, including oncology, immunology, organ transplantation, and neurodegenerative diseases, while making our tools accessible to the research and medical communities. Our lab is part of the UCI School of Medicine which includes the Chao Family
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