Postdoctoral Fellow

The Cardiovascular Data Science (CarDS) Lab at the Yale School of Medicine seeks a highly motivated Postdoctoral Associate to conduct research at the intersection of artificial intelligence, machine learning, clinical informatics, and cardiovascular medicine under the mentorship of Dr. Rohan Khera. The successful candidate will contribute to projects leveraging electronic health records, randomized clinical trial data, and other large-scale healthcare datasets to advance precision medicine, healthcare quality measurement, and cardiovascular disease diagnosis and prediction.

The Postdoctoral Associate will apply machine learning, natural language processing, and large language model (LLM) approaches to structured and unstructured healthcare data and support the development of AI-enabled tools and electronic quality measurement systems. Working within a multidisciplinary team of clinicians, data scientists, statisticians, and informaticians, the candidate will gain advanced training in study design, data science methods, model development and evaluation, scientific communication, and the translation of AI innovations into clinically meaningful applications. The position offers excellent opportunities for scholarly productivity, interdisciplinary collaboration, and professional growth in a leading research environment focused on the future of data-driven healthcare.

The CarDS lab is a multidisciplinary group of junior faculty, postdoctoral trainees, and graduate and undergraduate students across Yale. We collaborate with informatics, computer science, and statistics groups at Yale and several leading institutions nationally and internationally. We have developed novel tools to measure and improve care quality using data from electronic health records and software solutions for the early diagnosis of cardiovascular disorders. Our research offers unique collaborative opportunities with industry, health systems and health technology partners. For more information, please visit the CarDS Lab website.

Essential Duties:

1. Conduct independent and collaborative research focused on the application of machine learning and artificial intelligence methods to healthcare data, including data derived from randomized clinical trials and electronic health records.
2. Develop, implement, and evaluate predictive models, large language model (LLM) applications, and natural language processing tools to support precision medicine and healthcare quality improvement initiatives.
3. Curate, process, analyze, visualize, and interpret structured and unstructured healthcare data using advanced computational and statistical approaches.
4. Contribute to the design, execution, and management of research studies and clinical trials, including participant recruitment, enrollment, follow-up, and regulatory documentation as needed.
5. Collaborate with multidisciplinary teams of clinicians, data scientists, informaticians, statisticians, and trainees to advance research objectives and translate findings into clinically meaningful applications.
6. Support study conceptualization, protocol development, data analysis, and dissemination of findings through manuscripts, conference presentations, reports, and grant proposals.
7. Participate in the development and implementation of AI-enabled electronic quality measurement systems and EHR-based research workflows.
8. Maintain regular communication with the Principal Investigator regarding project progress, challenges, and opportunities for improvement.
9. Adhere to all Yale University, Yale School of Medicine, and research-related policies regarding data security, privacy, compliance, and responsible conduct of research.
10. Perform other duties related to the research mission of the CarDS Lab as assigned.

1. Medical or Doctoral Degree (MD or PhD or MD/PhD or equivalent), with previous experience in working with large datasets.
2. Prior experience with research, with a demonstrated track record, and prior scientific publication(s).

1. Demonstrated experience conducting research involving machine learning, artificial intelligence, statistical modeling, or advanced healthcare data analytics.
2. Proficiency in Python for data analysis, machine learning model development, and scientific computing.
3. Experience working with large healthcare datasets, electronic health records, clinical trial data, or other biomedical data sources.
4. Strong analytical, problem-solving, and quantitative research skills.
5. Excellent scientific writing and communication skills, including evidence of scholarly publications and presentations.
6. Ability to work effectively both independently and collaboratively within a multidisciplinary research environment.

1. Experience applying machine learning, large language models (LLMs), natural language processing, or deep learning techniques to healthcare or biomedical research problems.
2. Familiarity with randomized clinical trial data and precision medicine research.
3. Experience processing and analyzing unstructured clinical text and developing NLP-based applications.
4. Experience with data visualization, cloud computing environments, or scalable data processing workflows.
5. Interest in translating AI innovations into clinical practice and healthcare quality improvement.