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 postdoctoral fellow to work on the development of EEG-based closed-loop brain-computer interfaces (BCIs) for treatment and rehabilitation of a debilitating neurological disorder, dystonia.
The postdoctoral fellow will function as part of a multidisciplinary team of neuroscientists, neurologists, and laryngologists. 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 biotech industry.
The successful candidate will work at the intersection of computational neuroscience, machine learning, and neuroimaging to implement BCIs based on neurofeedback and virtual reality.
Responsibilities will include but may not be limited to
- Design of a virtual environment to convey auditory, visual and neurofeedback and implementation of a closed-loop BCI
- Development of machine-learning algorithms to transform high-density neural signals into neurofeedback to present through virtual reality environment
- Integration of data collection and signal processing algorithms
- Experimental data collection and signal processing
- Establishment of new and fostering of existing collaborations
- 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
- Experience in neural engineering, including BCIs, neurofeedback, neuroprosthetics
- Substantial experience with EEG data analysis
- Experience in biomedical signal processing, feature extraction and machine learning applied to biological and/or neural datasets
- Broad proficiency and experience with supervised and unsupervised machine-learning methods
- Advanced programming skills (Python and/or Matlab)
- Knowledge and experience of the cloud-based computational platforms (e.g., AWS)
- Strong experience in the algorithmic design, mathematical models, analysis and integration of dynamic systems
- Excellent verbal and written communication skills
- Strong publication record and academic credentials
- Ability to work effectively both independently and in collaboration with multiple investigators