"Postdoctoral Positions in AI for Medicine and Pathology"

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 Ph.D., 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, with renewal based on performance and available support.

The lab focuses on developing AI methods to improve patient diagnosis, prognosis, treatment response prediction, and treatment optimization. We are particularly interested in developing novel AI methods for analyzing pathology data, multimodal data fusion strategies, computational methods for rare diseases and new modalities (e.g., multiphoton microscopy), and translating AI solutions into clinical practice.

Minimum Qualifications: A doctoral degree in Computer Science, Biomedical Engineering, Bioinformatics, or a related field; strong understanding of machine learning and deep learning methods; proficiency in programming languages such as Python, C, C++, and deep learning libraries like PyTorch; demonstrated ability to work in a team-oriented environment; and a track record of first-author publications.

Application Requirements: Curriculum Vitae, sample of coding skills (e.g., link to GitHub), cover letter, contact information for 3 references, and up to 3 representative publications.

For more information, visit the lab website.