Research Fellow - Cardiovascular Medicine - Cardiovascular AI

Mayo Clinic • Rochester, MN

8d

About The Position

Dr. Chieh-Ju Chao’s team in the Department of Cardiovascular Medicine and the Mayo AI Lab is seeking a postdoctoral research fellow in cardiovascular artificial intelligence. The fellow 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, advancing systems from model development into clinical deployment. Its research is supported by the NIH/NHLBI and the Minnesota Partnership for Biotechnology and Medical Genomics, and it maintains an active collaboration with Stanford University. Fellows have access to a large echocardiography archive, dedicated GPU computing, and a clinical team using AI tools in routine echocardiography practice.

Research areas include vision-language models for echocardiography; agentic AI systems that coordinate imaging models, clinical data, and software tools; clinical deployment and prospective evaluation of AI in echo lab workflows; and multimodal patient representations and cardiac digital twins that combine echocardiography, cardiac imaging, and electronic health record data.

This is a temporary Research Fellow position intended to provide research training and education in a Mayo Clinic principal investigator’s program. Qualified candidates should show potential for research through their training and peer-reviewed publications and should become competitive for national research grants. Subject to a background check and CCATS Executive Committee approval, the fellow may have patient or research-subject contact directly related and incidental to the original research program. J-1 applicants in specified scholar, professor, specialist, and student intern categories sponsored by Mayo Clinic must provide proof of English proficiency.

A complete application requires a current curriculum vitae with bibliography and proof of doctoral degree, such as a diploma or final transcript with English translation if applicable, or a Registrar’s letter confirming completion of doctoral requirements and eligibility to graduate. Candidates should also be prepared to provide three letters of recommendation to the hiring PI.

Requirements

• 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.
• Hands-on experience training deep learning models on GPU clusters.
• Experience with cloud computing for machine learning; Google Cloud Platform (GCP), including Vertex AI, Compute Engine GPU instances, and Cloud Storage, is preferred.
• First-author peer-reviewed publications in AI or machine learning in journals or at conferences such as NeurIPS, ICML, ICLR, CVPR, or MICCAI.
• Submit a current curriculum vitae with bibliography and proof of doctoral degree or a Registrar’s letter confirming completion of doctoral requirements and eligibility to graduate.
• Provide proof of English proficiency if applying as a J-1 Short-Term Scholar, Research Scholar, Professor, Specialist, or Student Intern sponsored by Mayo Clinic.

Nice To Haves

• 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, such as RLHF, policy optimization methods, or 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; helpful but not required.
• Three letters of recommendation may be requested by the hiring PI.

Responsibilities

• Design, train, and evaluate deep learning models and agentic AI systems, including vision-language models, using large-scale echocardiographic video and clinical data and supervised training, post-training, and reinforcement learning.
• Build evaluation benchmarks and conduct 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 such as AHA, ACC, ASE, MICCAI, and NeurIPS.
• Contribute to grant applications.
• Work with engineers, cardiologists, and sonographers on a multidisciplinary team.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree