We are seeking a highly motivated Postdoctoral Research Fellow to join the Medical Intelligence lab and contribute to research at the intersection of medical imaging analysis and machine learning. The successful candidate will design, train, and validate deep learning models on clinical imaging data, publish in leading venues, and collaborate with clinicians, data scientists, and engineers. This role is well suited to someone who wants to translate methodological advances into tools with real clinical impact. This position centers on building large-scale foundation models for medical imaging and translating them into clinically meaningful applications. The work spans three connected threads: Large-scale vision-language model development. Design and train large multimodal vision-language models that jointly reason over medical images and associated text (reports, clinical notes, structured data), with an emphasis on scalable pretraining, efficient fine-tuning, and robust evaluation. Retinal imaging foundation model. Build and adapt a foundation model for retinal imaging (fundus photography and OCT) that can be pretrained on large image collections and transferred efficiently to a range of downstream tasks with limited labeled data. Downstream clinical applications. Apply and adapt these models to real-world clinical problems, including systemic and hematologic conditions such as multiple myeloma and sickle cell disease, where retinal and multimodal biomarkers may support early detection, risk stratification, and disease monitoring. The successful candidate will help move the group's work from general-purpose model development toward validated, clinically relevant tools, working closely with clinical collaborators throughout.
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Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree