Postdoctoral Fellow- Medical Imaging AI

About the role

We are seeking a highly motivated Postdoc with expertise in artificial intelligence (AI), machine learning, and magnetic resonance imaging (MRI) to support and advance cutting-edge translational research in cardiovascular imaging.

Responsibilities

• Research and develop deep learning models for MRI reconstruction, segmentation, quantification, and image enhancement
• Design, optimize, and evaluate AI pipelines to improve image acquisition efficiency, image quality, and robustness
• Integrate AI methods with quantitative CMR imaging biomarkers, including myocardial blood flow, strain, and tissue characterization
• Develop and apply methods for multi-modality data integration, combining imaging, physiologic signals (e.g., ECG), genetic, and clinical data for diagnostic and prognostic modeling
• Perform rigorous model validation, including reproducibility testing, bias assessment, and external validation
• Design, develop, and implement user-friendly software platforms for clinical deployment of AI-enabled imaging tools
• Serve as a technical lead on funded research projects (NIH R01s, industry collaborations)
• Contribute to study design, statistical analysis plans, and imaging endpoints
• Lead or co-author high-impact manuscripts, abstracts, and grant submissions
• Mentor trainees (PhD students, postdocs, research staff)
• Build and maintain scalable AI/MRI pipelines (Python, PyTorch/TensorFlow, etc.)
• Work with large-scale imaging datasets and HPC/GPU environments (H200s GPUs)
• Collaborate on data harmonization, curation, and governance across multi-site studies
• Partner closely with cardiologists, radiologists, MR physicists, and industry collaborators
• Support translation of AI tools toward clinical feasibility and regulatory readiness
• Present work at national and international scientific meetings

Qualifications

• PhD in Biomedical Engineering, Computer Science, Electrical Engineering, Medical Physics, or related field
• 5+ years of research experience in machine learning / deep learning / computer vision
• Demonstrated experience with medical imaging data
• Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow)
• Proven track record of publications in top-tier imaging journals (e.g., Radiology, Radiology: AI, Radiology: CTI, MRM, JCMR, MICCAI, IEEE TMI, IEEE TBME, CVPR, Medical Image Analysis).