Site: Massachusetts Eye and Ear Infirmary


At Mass General Brigham, we know it takes a surprising range of talented professionals to advance our mission-from doctors, nurses, business people and tech experts, to dedicated researchers and systems analysts. As a not-for-profit organization, Mass General Brigham is committed to supporting patient care, research, teaching, and service to the community. We place great value on being a diverse, equitable and inclusive organization as we aim to reflect the diversity of the patients we serve.


At Mass General Brigham, we believe a diverse set of backgrounds and lived experiences makes us stronger by challenging our assumptions with new perspectives that can drive revolutionary discoveries in medical innovations in research and patient care. Therefore, we invite and welcome applicants from traditionally underrepresented groups in healthcare - people of color, people with disabilities, LGBTQ community, and/or gender expansive, first and second-generation immigrants, veterans, and people from different socioeconomic backgrounds - to apply.



Job Summary
We are seeking a highly qualified and motivated postdoctoral fellow to join our collaborative teams in the Department of Ophthalmology / Glaucoma division at Massachusetts Eye and Ear (MEE), Harvard Medical School, to work on innovative projects that utilize machine learning and data science to understand mechanisms of glaucoma, a common eye disease. The successful candidate should have an MD or PhD in a relevant field such as biostatistics, computer science, machine learning or a related quantitative field, and will be interested to apply and develop computational and statistical methods for analyzing large-scale clinical, imaging and genomic data. The ideal candidate will have strong programming skills, a solid statistical understanding, and experience with large-scale data analysis, and will be excited to contribute to advancing the scientific discovery and medicine of eye disease. The candidate will work with Dr Zebardast as part of a vibrant research community of clinician scientists, biostatisticians, computational biologists, and machine learning engineers at MEE, an international leader for treatment and research in Ophthalmology and Otolaryngology. Being a member of our group will provide the opportunity to contribute to impactful projects and large collaborative efforts in the field of ocular data science. To learn more about our lab research directions please visit: If interested, please send your CV, a cover letter describing your previous research experience and future research interests, and contact information for 3 references, to Dr. Nazlee Zebardasrt : Nazlee_Zebardast [at] meei.harvard.edu. CHARACTERISTIC DUTIES: - Work both on independent and collaborative ophthalmic data science projects - Critically review, analyze, and communicate results to our team and collaborators. - Summarize work for publications and presentations - Organize all scripts in a publicly available repository (e.g., github) with clear documentation.



Qualifications


- Possess an MD or PhD preferably with a background in mathematics, computational science, computer science, statistics, machine learning, biomedical engineering, bioinformatics, visual science and ophthalmology or a related field. Fluency in written and spoken English is essential. - Strong programming skills and in-depth experience with several programming languages required, e.g. Python, R, C++; Experience with Cloud Computing a plus. - Experience with Unix/Linux environments, including shell scripting - Research experience with large-scale data analysis - Have a strong and productive publication record. - Demonstrate critical thinking, rigorous work, and ability to meet deadlines. - Strong personal skills, and excellent organization and verbal and written communication skills. - Ability to work effectively both independently and collaboratively in a fast-paced, academic environment and evolving field. WORKING CONDITIONS: The Zebardast lab is located in the main hospital building of Mass Eye and Ear, 243 Charles Street, in a research and office workspace that is part of MEE. The postdoctoral fellow will work in an office space adjacent to other biostatisticians and students and will be part of a larger multidisciplinary research environment. There is convenient access to MEE cafeteria on the 7th floor and, more importantly, abundant opportunities to interact with other MEE research groups and the Medical and Population Genetics community at the Broad Institute. The position will be hybrid with both remote and in-person components. PHYSICAL REQUIREMENTS: None.



Additional Job Details (if applicable)


Additional Job Description



Remote Type


Hybrid



Work Location


243-245 Charles Street



Scheduled Weekly Hours


40



Employee Type


Regular



Work Shift


Day (United States of America)



EEO Statement:


Massachusetts Eye and Ear Infirmary is an Affirmative Action Employer. By embracing diverse skills, perspectives and ideas, we choose to lead. 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.



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.