The Dystonia and Speech Motor Control Laboratory (https://simonyanlab.hms.harvard.edu) at Harvard Medical School, Massachusetts Eye and Ear, and Massachusetts General Hospital is seeking a computer science/machine-learning postdoctoral fellow to work on machine-learning algorithms for automatic diagnosis of dystonia and other movement disorders, prediction of the risk for dystonia development, and identification of treatment outcome. This work will be directly related to the extension of our recently developed DystoniaNet platform and will include brain MRI and EEG datasets and voice and speech recordings from healthy individuals and those with movement disorders.
The postdoctoral fellow will function as part of a multidisciplinary team of physician-scientists, neuroscientists, geneticists, neurologists, and laryngologists at Mass Eye and Ear and Mass General Hospital. This position is best suited for an individual with a broad computational background interested in understanding and examining critical clinical problems and developing research solutions for their translation into the clinical setting to improve healthcare. The fellow will be highly competitive to pursue future opportunities in either academia or industry (pharma and biotech).
The successful candidate will work at the intersection of machine learning, computational neuroscience, and neuroimaging to refine and validate clinically applicable algorithms.
Responsibilities will include but may not be limited to
- Development and refinement of deep learning and other benchmark algorithms for predictive classification of diseases
- Development of data-fusion algorithms capable of making inferences from multimodal datasets (e.g., fMRI and EEG data)
- Clinical translation and implementation of developed algorithms and interactions with clinicians for their testing
- Establishment of new and fostering of existing collaborations
- Participation in the regulatory aspects for clinical translation and patenting
- Presentation of the results at the scientific meetings and publication of journal articles
- Participation in grant writing and preparation
- Mentoring of junior staff
Qualifications and Skills
- PhD in computer science, neuroscience, biomedical engineering, or related fields
- Broad proficiency and experience with supervised and unsupervised machine-learning methods, experience building neural network architectures
- Substantial experience with EEG and MRI analysis
- Advanced programming skills (Python and/or Matlab), including deep learning packages (e.g., TensorFlow or Keras)
- Knowledge and experience of the 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