Postdoctoral Fellow in AI/ML for Maternal and Perioperative Care
Mass General Brigham
Summary
The Kovacheva Lab brings together clinicians, data scientists, and researchers to develop and evaluate computational approaches that improve care during pregnancy, childbirth, and the perioperative period. Our goal is to move rigorous scientific discoveries from model development through evaluation and integration into clinical workflows, including electronic health record– and device-based applications.
Job description
About Our Lab
The Kovacheva Lab brings together clinicians, data scientists, and researchers to develop and evaluate computational approaches that improve care during pregnancy, childbirth, and the perioperative period. Our goal is to move rigorous scientific discoveries from model development through evaluation and integration into clinical workflows, including electronic health record– and device-based applications.
The Opportunity
We are seeking an ambitious, hands-on postdoctoral fellow with strong AI/ML expertise and demonstrated research accomplishments who is eager to lead original research with meaningful clinical impact. You will help shape the lab’s computational research directions and develop a distinctive research program, supported by clinical and technical mentorship and an existing clinical data platform.
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. Specific projects will be developed jointly around your strengths and interests, aligned with the lab’s clinical priorities and funded research aims.
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. You will formulate research questions, design rigorous experiments, develop and validate models, and lead manuscripts. The position offers opportunities to grow as an independent scientist, mentor junior colleagues, and collaborate with clinical and computational researchers across Mass General Brigham, Harvard Medical School, the Broad Institute, and industry.
Qualifications
- A PhD in computer science, engineering, statistics, applied mathematics, physics, or another quantitative discipline, completed before the position start date.
- Strong Python programming skills and hands-on experience developing and evaluating machine-learning or deep-learning models.
- Demonstrated research expertise in machine learning or deep learning, with methodological strengths relevant to areas such as time-series modeling, computer vision, representation learning, or multimodal learning.
- Evidence of scientific ownership, including leading a research 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.
- Intellectual curiosity, clear communication, a collaborative approach, and interest in mentoring and interdisciplinary clinical research.
- Experience with physiological waveforms, ultrasound, other medical imaging, or clinical data is welcome but not required.
Position Details
Appointment and funding: The initial appointment is for one year, renewable annually subject to satisfactory performance and institutional requirements. The research program has five years of funding.
Location and work arrangement: Boston, Massachusetts. This is a hybrid position with on-site participation at Brigham and Women’s Hospital.
Salary: $73,544–$81,179 annually, based on the NIH postdoctoral stipend scale and years of relevant experience, consistent with institutional policies.
Visa sponsorship: J-1 visa sponsorship is available for eligible candidates, subject to institutional requirements.
Preferred start date: November 1, 2026. Applications will be reviewed on a rolling basis until the position is filled.
Requirements
- PhD in computer science, engineering, statistics, applied mathematics, physics, or another quantitative discipline, completed before the position start date.
- Strong Python programming skills and hands-on experience developing and evaluating machine-learning or deep-learning models.
- Demonstrated research expertise in machine learning or deep learning, with methodological strengths relevant to areas such as time-series modeling, computer vision, representation learning, or multimodal learning.
- Evidence of scientific ownership, including leading a research 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.
- Intellectual curiosity, clear communication, a collaborative approach, and interest in mentoring and interdisciplinary clinical research.
Company overview
Mass General Brigham is one of the largest integrated healthcare delivery systems in the United States, formed through the merger of Massachusetts General Hospital and Brigham and Women's Hospital. Based in Boston, Massachusetts, this academic medical center system operates multiple hospitals, specialty facilities, and community health centers throughout New England. The organization is renowned for its world-class patient care, cutting-edge medical research, and comprehensive medical education programs. As a teaching affiliate of Harvard Medical School, Mass General Brigham trains the next generation of healthcare professionals while advancing medical science through groundbreaking research initiatives. The system provides a full continuum of healthcare services, from primary care and preventive medicine to complex specialty treatments and emergency services. With a focus on innovation and patient-centered care, Mass General Brigham consistently ranks among the top hospital systems in the nation for clinical excellence and research output.