Site: Massachusetts Eye and Ear Infirmary
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
The Postdoctoral Fellow will support and help lead a multidisciplinary research initiative focused on advancing laryngology through real-time machine learning, computer vision, and quantitative analysis of laryngoscopy videos. This role will involve developing, testing, validating, and translating algorithms that track laryngeal anatomy, classify examination states, extract clinically meaningful video-derived metrics, and support future clinical decision-support tools. The fellow will work with laryngologists, clinical research staff, engineers, and collaborators at Mass Eye and Ear and Mass General Brigham to move the project from proof-of-concept research toward reproducible, clinically useful deployment. This position is best suited for a highly independent, technically strong researcher with broad computer science or machine learning expertise who is interested in applying advanced AI methods to important clinical problems in laryngology.
Qualifications
ESSENTIAL FUNCTIONS:
Data Collection & Processing:
Research & Analysis:
Publication & Dissemination:
Clinical & Translational Research Support:
General Research Support:
EDUCATION AND EXPERIENCE:
PhD or equivalent degree in computer science, biomedical engineering, electrical engineering, computational neuroscience, data science, statistics or a closely related field.
Strong experience with modern machine learning methods, including deep learning, neural network architecture design, computer vision, video analysis, temporal modeling, supervised and/or self-supervised learning, and rigorous model evaluation strategies.
Advanced programming skills in Python are required; experience with PyTorch and/or TensorFlow/Keras, OpenCV, Git, Linux/Unix environments, high-performance or cloud-based computing platforms with GPU acceleration, and reproducible research workflows is strongly preferred. Experience with DeepLabCut software is also appreciated.
Excellent verbal and written communication skills, strong publication record or evidence of scholarly productivity, ability to work independently, and interest in collaborating with clinicians to translate AI research into healthcare applications.
Pay Range: $70,000.00 - $71,750.00/Annual
Additional Job Details (if applicable)
Remote Type
Onsite
Work Location
243-245 Charles Street
Scheduled Weekly Hours
40
Employee Type
Regular
Work Shift
Day (United States of America)
EEO Statement:
5110 Massachusetts Eye and Ear Infirmary is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. To ensure reasonable accommodation for individuals protected by Section 503 of the Rehabilitation Act of 1973, the Vietnam Veteran's Readjustment Act of 1974, and Title I of the Americans with Disabilities Act of 1990, applicants who require accommodation in the job application process may contact Human Resources at (857)-282-7642.
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.