À propos de ce poste Research Fellow chez Mass General Brigham
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 focus of this fellowship is an ongoing research project focused on creating, testing and validating AI methods to assist clinicians in detecting errors (false outputs) generated by multimodal AI models. The current focus of the work involves use of mechanistic interpretability techniques in the natural language domain (LLM).Research Group: The Multi-Modal AI in Neuroimaging (MMAIN) Lab is a multi-institutional multi-disciplinary collaboration between faculty of MGB, the Northeastern University Khoury College of Computer Science (NU) and Dana Farber Cancer Institute (DF).
Faculty: Geoffrey Young MD (MGB), Jisoo Kim MD (MGB), Byron Wallace PhD (NU), Huaizu Jiang PhD (NU), Lei Qin PhD (DF)
Qualifications
IMPORTANT – APPLICATION INSTRUCTIONS
This is a courtesy posting. Please do not submit your application through the Workday "Apply" button.
To be considered for this position, email your CV and cover letter describing your research interests and goals, a full list of publications to the hiring manager Kizzy Scott at kscott9@bwh.harvard.edu
Applications submitted only through Workday will not be forwarded to the hiring manager and may not be reviewed.
MD or equivalent degree and/or experience in clinical medical practice (eg internship, residency or fellowship training) is highly desirable. Medical students who have appropriate clinical experience and/or candidates with other educational backgrounds and clinical experience are also encouraged to apply. No computer science, data science or coding background is required as training and close collaboration with AI researchers will be provided. The focus of the research will be to work collaboratively with computer scientists and clinicians to assist in designing, coordinating, carrying out, and analyzing domain expert clinician annotations and assessments of the mechanistic interpretability methods in clinical medicine.
What we offer: Through this fully funded 1 year fellowship, you will gain research and career mentorship from clinician and AI faculty at Harvard-affiliated institutions. With exposure to state-of-the-art multimodal AI and LLM interpretability research, you will have opportunities to publish in high-impact journals and present at national and international meetings. This will provide you with a strong foundation for future residency, fellowship, physician-scientist, or industry careers in clinical AI.
Additional Job Details (if applicable)
The compensation range for this role is between $71,750 - $81,179. Candidate experience is thoughtfully considered throughout the recruitment process, and those with significant experience may be considered for compensation above the starting range listed, as consistent with organizational guidelines and internal equity
Years of experience
0 $71,750 - $73,544
1 $73,544 - $75,382
2 $75,382 - $77,267
3 $77,267 - $79,199
4+ $79,199 - $81,179
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.
