Postdoctoral Fellowship in Cardiovascular Artificial Intelligence & Electrophysiology
About the Lab
Located within NYU Langone’s Heart Rhythm Center, our multidisciplinary research group works at the intersection of clinical medicine, biomedical engineering, and advanced artificial intelligence. Led by the Director of the Inherited Arrhythmia Program and the lead for Cardiology AI efforts, the lab is focused on translating cutting-edge machine learning and agentic LLM workflows into practical clinical tools.
Current areas of emphasis include automated ECG signal analysis, genetic variant interpretation, and streamlining clinical documentation. We operate in a highly entrepreneurial, fast-paced environment with access to advanced computing resources and close collaboration with industry partners.
The Role
We are seeking a highly motivated Postdoctoral Researcher with a strong background in machine learning, software engineering, and informatics to help build next-generation AI applications in cardiology.
The successful candidate will lead technical development and validation across projects ranging from ECG signal analysis tools to multi-agent systems for automating clinical workflows and enhancing genetic reporting. This role offers the opportunity to build end-to-end applications, from algorithmic conception through deployment and clinical validation.
Key Responsibilities
- Design, train, and validate machine learning models for complex physiological signals, including ECGs, CT, MRI, tabular, and genomic data.
- Develop and configure advanced AI agent frameworks for clinical documentation and decision support workflows.
- Write robust backend code in Python and build intuitive frontend interfaces in React and related tools for prototype deployment and clinical validation.
- Contribute to high-impact publications, patent applications, and federal grant submissions, including programs such as AHA Strategically Focused Research Networks.
- Collaborate with clinicians, data scientists, and industry partners to commercialize and scale health AI solutions.
Qualifications
Education
Master’s or PhD in Computer Science, Applied Physics, Medical Informatics, Data Science, or a closely related quantitative field. Experience with frontier machine learning model development is a plus.
Technical Skills
- Expertise in Python and modern machine learning or deep learning frameworks such as PyTorch and TensorFlow.
- Hands-on experience building, training, fine-tuning, and deploying CNNs, RNNs, Transformers, or related model architectures.
- Experience configuring and deploying large language models and agentic workflows is a plus.
- Proficiency in full-stack development, especially React-based frontend prototyping.
Domain Knowledge
Previous experience with clinical datasets, medical imaging, ECG signal processing, or clinical genetics is highly preferred.
Attributes
An entrepreneurial mindset, strong independent problem-solving ability, and a desire to translate academic research into clinically meaningful and commercially viable products.
How to Apply
Please submit a cover letter describing your research interests and technical background, a current CV, and contact information for references.