Postdoctoral Fellow

Description

The University of Rochester School of Nursing is seeking a highly motivated and skilled Postdoctoral Fellow to join a multidisciplinary team focused on advancing cardiovascular research through machine learning and biomedical signal processing. This one-year position will provide an exciting opportunity to work at the intersection of computational modeling, clinical data science, and cardiology.

The fellow will work closely with a designated primary mentor and a collaborative team of clinical and engineering investigators. The role will involve developing artificial intelligence and machine learning models to predict cardiovascular disease outcomes from biomedical signals (e.g., ECGs) and electronic health records (EHRs), with an emphasis on implementing scalable pipelines in a cloud-based Azure environment.

The ideal candidate will have a PhD in biomedical informatics, computer science, or a related field, with prior experience working with healthcare data and a strong foundation in signal processing, machine learning, and cloud computing.

Primary Responsibilities:

• Develop and evaluate machine learning models to predict cardiovascular events using physiological signals and clinical datasets.
• Perform advanced signal processing and feature extraction from ECG and other time-series data.
• Implement reproducible, cloud-based pipelines in Microsoft Azure for model training and deployment.
• Collaborate on high-impact publications and present findings at scientific conferences.
• Contribute to grant applications and the development of an independent research program aligned with cardiovascular data science.
• Participate in interdisciplinary team meetings with faculty, clinicians, and data scientists.

Qualifications

• A Ph.D. in biomedical informatics, computer science, electrical/biomedical engineering, or a related field.
• Demonstrated experience in signal processing, machine learning, or computational modeling, preferably in a healthcare context.
• Strong programming skills in Python, R, or MATLAB.
• Experience working with large clinical datasets or electronic health records.
• Familiarity with cloud platforms such as Microsoft Azure or AWS.
• Strong oral and written communication skills, with a track record of academic publications.
• Ability to work independently and collaboratively within a multidisciplinary team. 

Preferred Qualifications:

• Experience with cardiovascular data (e.g., ECGs, hemodynamic monitoring).
• Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Knowledge of regulatory and ethical considerations in clinical data use.

Application Instructions

If you already have an Interfolio account, please sign in to apply to this position. If not, please create an Interfolio account. For questions/concerns pertaining to the position, email erin_malley@urmc.rochester.edu.

Equal Employment Opportunity Statement

EOE, including disability/protected veterans

The University of Rochester is committed to fostering, cultivating, and preserving an inclusive and welcoming culture to advance the University’s Mission to Learn, Discover, Heal, Create – and Make the World Ever Better. In support of our values and those of our society, the University is committed to not discriminating on the basis of age, color, disability, ethnicity, gender identity or expression, genetic information, marital status, military/veteran status, national origin, race, religion/creed, sex, sexual orientation, citizenship status, or any other characteristic protected by federal, state, or local law (Protected Classes). This commitment extends to non-discrimination in the administration of our policies, admissions, employment, access, and recruitment of candidates for all persons consistent with our values and based on applicable law.