Postdoctoral Fellow AI/ML for Physiological Waveforms and Ultrasound

Postdoctoral Fellow – AI/ML for Physiological Waveforms and Ultrasound

Brigham and Women’s Hospital, a teaching hospital of Harvard Medical School

About Our Lab

Help shape the future of AI for maternal health. The Kovacheva Lab brings the creativity and momentum of a startup to the collaborative research environment of Brigham and Women’s Hospital and Harvard Medical School. Our team unites clinicians, data scientists, and researchers around a shared goal: moving rigorous computational discoveries from model development through evaluation and integration into clinical workflows, including EHR- and device-based applications, to support safer pregnancies and childbirth.

Role

We are seeking an ambitious, hands-on postdoctoral fellow eager to lead original research with meaningful clinical impact. You will have opportunities to work with a large-scale clinical data platform containing billions of data points from more than 300,000 patients, subject to applicable research, privacy, and data-governance approvals, and to build a distinctive, publishable research program.

Potential projects include physiological waveform modeling, AI for ultrasound and other medical imaging, multimodal learning, and early prediction of hypertensive crises, hemodynamic instability, and hemorrhage. You will define hypotheses, design rigorous studies, build and validate models, lead manuscripts, and collaborate closely with clinical and technical mentors.

Grow as an independent scientist while mentoring junior colleagues and collaborating across Mass General Brigham, Harvard Medical School, the Broad Institute, and industry. We welcome researchers from a wide range of quantitative and computational fields who are excited to learn, think creatively, and take ownership of important ideas.

Position Details

Location and start: Boston, Massachusetts. Preferred start is as soon as possible.

Annual base salary range: $60,000–$70,000, depending on relevant experience and qualifications.

Required Qualifications

• PhD in computer science, engineering, statistics, applied mathematics, physics, or another quantitative discipline, completed or expected to be conferred before the position start date.
• Strong Python programming and hands-on experience developing and evaluating machine-learning or deep-learning models.
• Hands-on expertise in at least one of the following: physiological signal processing or time-series analysis; ultrasound or other medical-image analysis; or multimodal modeling involving signals, images, and clinical data.
• Demonstrated ability to formulate research questions, design rigorous experiments, validate models, and communicate original scientific work.
• Demonstrated scientific ownership, including leading at least one project from question formulation through rigorous analysis and scientific communication.
• Evidence of original research productivity, such as first-author peer-reviewed publications or substantial preprints.
• Collaborative spirit, intellectual curiosity, clear communication, and interest in mentoring and cross-functional clinical research.

Experience with clinical data is preferred but not required.

How to Apply

Email vkovacheva at bwh.harvard dot edu with the subject “Application for AI/ML Postdoctoral Fellow position” and include your CV; a brief cover letter describing your research interests, scientific contributions, fit for the role, and availability to start; and links to one or two representative publications or preprints.