Research Fellow - Cardiovascular Medicine - Cardiovascular AI
Employer not yet confirmed · Rochester, Minnesota, United States
Dr. Chieh-Ju Chao's team, based in the Department of Cardiovascular Medicine and the Mayo AI Lab, has an opening for a postdoctoral research fellow in cardiovascular artificial intelligence. The individual will train in Dr. Chao's research program and work closely with faculty of the Mayo Clinic Echocardiography Laboratory.
The team develops AI for echocardiography and multimodal cardiovascular data and takes it from model development into clinical deployment. The team is supported by the NIH/NHLBI and the Minnesota Partnership for Biotechnology and Medical Genomics and has an active research collaboration with Stanford University. Fellows have access to one of the largest echocardiography archives in the world, dedicated GPU computing, and a clinical team that runs AI tools in day-to-day echo lab practice. Current research areas include:
• Vision-language models for echocardiography: foundation and multimodal large language models, developed through large-scale training, post-training, and reinforcement learning, that read echocardiographic videos and produce quantitative measurements and clinical interpretations.
• Agentic AI systems: AI agents that coordinate imaging models, clinical data, and software tools to support physicians in echocardiographic interpretation and reporting.
• Clinical deployment and prospective evaluation: bringing AI into the Mayo echo lab workflow, running reader studies and prospective studies of physician and AI interaction, and monitoring how models perform in practice.
• Multimodal patient representation and cardiac digital twins: combining echocardiography, cardiac imaging, and electronic health record data to model disease course and predict outcomes.
Responsibilities
• Design, train, and evaluate deep learning models and agentic AI systems, including vision-language models, on large-scale echocardiographic video and clinical data, using supervised training, post-training, and reinforcement learning.
• Build evaluation benchmarks and run internal and external validation studies.
• Contribute to clinical deployment studies, including data pipelines, performance monitoring, and statistical analysis.
• Lead first-author manuscripts and present at cardiology and machine learning meetings (e.g., AHA, ACC, ASE, MICCAI, NeurIPS).
• Contribute to grant applications and work with engineers, cardiologists, and sonographers on a multidisciplinary team.
The following is required for completeness of application:
• Current curriculum vitae with bibliography
• Proof of doctoral degree (copy of diploma or final transcript with English translation, if applicable) or a letter from your university's Registrar office indicating you have fulfilled all doctoral program requirements and are eligible to graduate.
- Please also be prepared that you may be asked to provide three letters of recommendation to the hiring PI
A Research Fellow at Mayo Clinic is a temporary position intended to provide training and education in research. Individuals will train in the research program of a Mayo Clinic principal investigator. Qualified individuals will demonstrate the potential for research as evidenced by their training and peer-reviewed publications and should become competitive for national research grants. Upon background check and CCATS Executive Committee approval, Research Fellow may have patient/research subject contact directly relating to and incidental to the original research program. Proof of English proficiency is required for J-1 Short-Term Scholars, Research Scholars, Professors, Specialists, and Student Interns sponsored by Mayo Clinic.
- PhD in computer science, electrical or biomedical engineering, statistics, or a related quantitative field, or an MD with substantial computational research experience.
- Strong programming skills in Python and PyTorch, and hands-on experience training deep learning models on GPU clusters.
- Experience with cloud computing for machine learning, preferably Google Cloud Platform (GCP), such as Vertex AI, Compute Engine GPU instances, and Cloud Storage.
- First-author peer-reviewed publications in AI or machine learning, in journals or at conferences (e.g., NeurIPS, ICML, ICLR, CVPR, MICCAI).
Preferred:
- Experience with multimodal models, large language models, or vision-language models, including training, fine-tuning, and post-training.
- Experience with reinforcement learning for model post-training (e.g., RLHF, policy optimization methods, reward design).
- Experience building LLM-based agents with tool use.
- Experience with medical imaging or video data (DICOM experience is a plus).
- Experience with distributed training and HPC environments.
- Familiarity with cardiovascular imaging is helpful but not required.