Mass General Brigham · 1 month ago
Research Fellow - Deep Learning
Mass General Brigham is a not-for-profit organization that supports patient care, research, teaching, and community service. They are seeking a postdoctoral fellow to work on machine-learning algorithms for automatic diagnosis and treatment of dystonia, collaborating with a multidisciplinary team to implement solutions in clinical settings.
Health CareHome Health CareMedical
Responsibilities
Experimental data collection and processing
Development and refinement of deep learning and other benchmark algorithms for predictive classification of dystonia and other related disorders
Clinical translation and implementation of the developed algorithms and interactions with clinicians for their testing
Establishment of new and fostering of existing collaborations
Participation in the regulatory aspects of clinical translation and patenting
Presentation of the results at the scientific meetings and publication of journal articles
Mentoring junior staff
Qualification
Required
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, expertise in building 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
Company
Mass General Brigham
Mass General Brigham specializes in providing medical treatments and health diagnostics services.
H1B Sponsorship
Mass General Brigham has a track record of offering H1B sponsorships. Please note that this does not
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2025 (77)
2024 (61)
2023 (93)
2022 (70)
2021 (80)
2020 (29)
Funding
Current Stage
Late StageLeadership Team
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