Research Fellow

OverviewMass 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 SummaryPostdoctoral Fellow in Deep LearningWe have an open position for a computer science/machine-learning postdoctoral fellow to work on machine-learning algorithms for automatic diagnosis of dystonia, prediction of the risk for dystonia development, and the efficacy of treatment outcomes. This work will be directly related to the extension of our recently developed DystoniaNet platform and will include brain MRI datasets from patients with dystonia, other movement disorders, and healthy individuals.QualificationsThe postdoctoral fellow will be part of a multidisciplinary team of neuroscientists, neurologists, laryngologists, and geneticists at Mass Eye and Ear and Mass General Hospital and work at the intersection on the development, testing and implement of DystoniaNet in the clinical setting. This position is best suited for an individual with a broad computer science background interested in understanding and examining critical clinical problems and developing research solutions for their translation to healthcare. The fellow will be highly competitive to pursue future opportunities in either academia or industry (pharma and biotech).ResponsibilitiesExperimental data collection and processingDevelopment and refinement of deep learning and other benchmark algorithms for predictive classification of dystonia and other related disordersClinical translation and implementation of the developed algorithms and interactions with clinicians for their testingEstablishment of new and fostering of existing collaborationsParticipation in the regulatory aspects of clinical translation and patentingPresentation of the results at the scientific meetings and publication of journal articlesMentoring junior staffQualifications and SkillsPhD or an equivalent degree in computer science, neuroscience, biomedical engineering, or related fieldsBroad proficiency and experience with supervised and unsupervised machine-learning methods, expertise in building neural network architecturesExperience with neuroimaging data processingAdvanced programming skills (Python and/or Matlab), including deep learning packages (e.g., TensorFlow or Keras)Knowledge and experience with cloud-based computational platforms (e.g., AWS)Excellent verbal and written communication skillsStrong publication record and academic credentialsAbility to work effectively both independently and in collaboration with multiple investigatorsRemote TypeOnsiteWork Location243-245 Charles StreetScheduled Weekly Hours40Employee TypeRegularWork ShiftDay (United States of America)EEO StatementMassachusetts 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 FrameworkAt 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. #J-18808-Ljbffr