Research Fellow - Deep Learning
Research Fellow - Deep Learning
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Job Description
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
Postdoctoral 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 developedDystoniaNet platformand will include brain MRI datasets from patients with dystonia other movement disorders and healthy individuals.
Qualifications
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 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).
Responsibilitiesinclude but may not be limited to
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
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 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
Additional Job Details (if applicable)
Postdoctoral Fellow in Deep Learning
We 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 developedDystoniaNet platformand will include brain MRI datasets from patients with dystonia other movement disorders and healthy individuals.
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 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).
Responsibilitiesinclude but may not be limited to
- 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
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 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
Remote Type
Work Location
Scheduled Weekly Hours
Employee Type
Work Shift
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.
Employment Type
Full-Time
