Research Fellow - Deep Learning

Overview Mass General Brigham seeks a Postdoctoral Fellow in Deep Learning 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 extend the DystoniaNet platform and use brain MRI datasets from patients with dystonia, other movement disorders, and healthy individuals. The position is part of a multidisciplinary team at Mass Eye and Ear and Mass General Hospital. Responsibilities Experimental data collection and processing Development and refinement of deep learning and other benchmark algorithms for predictive classification of dystonia and related disorders Clinical translation and implementation of the developed algorithms and interactions with clinicians for testing Establishment of new and fostering of existing collaborations Participation in regulatory aspects of clinical translation and patenting Presentation of results at scientific meetings and publication of journal articles Mentoring junior staff Qualifications and Skills PhD or an equivalent degree in computer science, neuroscience, biomedical engineering, or related fields Broad proficiency and experience with supervised and unsupervised machine-learning methods, training of neural network architectures Experience with neuroimaging data processing Advanced 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 skills Strong publication record and academic credentials Ability to work effectively both independently and in collaboration with multiple investigators Additional Job Details (if Applicable) The 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 will work at the intersection on the development, testing, and implementation of DystoniaNet in the clinical setting. This position is suited for an individual with a broad computer science background interested in solving critical clinical problems and translating research into healthcare. The fellow is expected to pursue future opportunities in academia or industry (pharma and biotech). 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 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 reasonable accommodation for individuals with a disability to participate in the job application or interview process, perform essential job functions, and receive other benefits and privileges of employment. For accommodations related to Section 503 of the Rehabilitation Act, Vietnam Veteran’s Readjustment Act, and the Americans with Disabilities Act, applicants who require accommodation may contact Human Resources. Mass General Brigham Competency Framework At Mass General Brigham, our competency framework defines effective leadership by specifying critical behaviors for successful performance. The framework comprises ten competencies and is used to evaluate performance, hiring decisions, and development needs.

#J-18808-Ljbffr