- Salary
- $74k – $81k
- Location
- 45 Francis Street Boston (Surgery Building), United States of America
- Workplace
- Onsite
- Type
- Full-time
- Department
- Healthcare
- Education
- PhD
- Visa
- Sponsored
- Source
- Workday
Description
Mass General Brigham relies on a wide range of professionals, including doctors, nurses, business people, tech experts, researchers, and systems analysts to advance our mission. As a not-for-profit, we support patient care, research, teaching, and community service, striving to provide exceptional care. We believe that high-performing teams drive groundbreaking medical discoveries and invite all applicants to join us and experience what it means to be part of Mass General Brigham.
Job Summary
APPLY BY EMAIL ONLY - APPLICATION INSTRUCTIONS BELOW.About Our Lab
Help shape the future of AI for maternal and perioperative care. 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. We welcome strong computational researchers from a range of fields. Prior experience in maternal health or perioperative medicine is not required, and candidates are not expected to have expertise across all listed data modalities.
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. We value strong computational foundations, rigorous scientific thinking, and the ability and enthusiasm to learn new methods and clinical applications.
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.
Selected Lab Research
MERLIN: A scalable, collaborative platform to facilitate healthcare AI research. IEEE Journal of Biomedical and Health Informatics, 2023. PubMed
SPELL: A scalable method using regular expressions and large language models for clinical information extraction. Computer Methods and Programs in Biomedicine, 2026. PubMed
How to Apply
Please include your CV, a brief cover letter describing your research interests, scientific contributions, fit for the position, and availability to start; links to one or two representative publications or preprints
Additional Job Details (if applicable)
IMPORTANT – APPLICATION INSTRUCTIONS
Please do not apply through the Workday “Apply” button.
This position is being posted for informational purposes only. To be considered, please submit the following directly to the hiring manager by clicking the link below:
- CV/resume
- Cover letter describing your research interests, scientific contributions, fit for the position, and availability to start
- Links to one or two representative publications or preprints
Please note: Applications submitted through Workday will not be forwarded to the hiring manager and may not be reviewed.
Remote Type
Work Location
EEO Statement:
Mass General Brigham Competency Framework
At Mass General Brigham, our competency framework defines what effective leadership “looks like” by specifying which behaviors are most critical for successful performance at each job level. The framework is comprised of ten competencies (half People-Focused, half Performance-Focused) and are defined by observable and measurable skills and behaviors that contribute to workplace effectiveness and career success. These competencies are used to evaluate performance, make hiring decisions, identify development needs, mobilize employees across our system, and establish a strong talent pipeline.