Postdoctoral Fellow Positions in Multi-Modal Brain Imaging, Machine Learning, and Neural Modeling at Harvard Medical School

  • Postdoc
  • Boston
  • Posted 4 days ago
  • Expires on: March 15, 2018

Mass Eye and Ear/Harvard Medical School

Postdoctoral Fellow Positions in Multi-Modal Brain Imaging, Machine Learning, and Neural Modeling at Harvard Medical School

The Dystonia and Speech Motor Control Laboratory ( at Harvard Medical School has several open postdoctoral fellow positions to study normal and diseased organization of large-scale networks controlling highly skilled motor behaviors. We employ multi-modal neuroimaging methodologies, including task-production, resting-state and pharmacological fMRI, high-resolution structural MRI, diffusion weighted imaging, and intracranial EEG, to examine brain networks in healthy individuals and patients with dystonia and epilepsy. Our analytic approaches include graph theoretical analysis of large-scale neural networks, machine learning algorithms for diagnostic and predictive classification of neurological disorders, and neural population modeling. The postdoctoral fellow may work on one or more projects.


* Machine learning for identification and validation of neuroimaging and genetic markers of dystonia and the prediction of risk for dystonia development.

* Graph theoretical analysis, machine learning and pharmacogenomics for identification of primary mechanistic markers of action of a novel oral medication in dystonia.


* Graph theoretical analysis of intracranial EEG for identification of topology of large-scale neural connectivity in epilepsy and the development of neural markers for prediction of epileptic seizures.

Normal speech production

* Development and implementation of large-scale neural population models incorporating neurotransmission for simulation and prediction of brain activity during speech production.

Minimum Qualifications

  • PhD in computer science, mathematics, bioengineering, neuroscience, or related fields
  • Exceptionally strong computational and biostatistical skills
  • Solid experience in Python, MATLAB, and C
  • Strong experience in algorithmic design, mathematical models, and analysis and integration of dynamic systems
  • A proven track record of productive research and excellent academic credentials

The application should be sent to Dr. Kristina Simonyan ( and include a cover letter, CV, a statement of research interests, and the names and contact information of three references.