Two NIH BRAIN Initiative funded computational postdoctoral positions available: Brown and Mass. General Hospital
The field of neuroscience is experiencing unprecedented growth in the ability to record from and manipulate brain circuits in humans and in animal models. MEG/ EEG are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it is still extremely difficult to interpret the underlying cellular and circuit level generators of these ‘macro-scale’ signals without simultaneous invasive recordings. Supported by a BRAIN Initiative grant through NIH we have started a collaborative project between Brown University, Yale University and Massachusetts General Hospital (MGH) to develop a new software tool for circuit level interpretation of human brain activity measured with MEG/EEG. We are hiring two post-docs to join our team.
A three-year postdoctoral position at Brown University
PI: Dr. Stephanie Jones contact: Stephanie_Jones@brown.edu
A three-year postdoctoral position at Massachusetts General Hospital
PI: Dr. Matti Hämäläinen contact: email@example.com
The post-docs hired will work among a team of researcher at Brown University, MGH, and Yale University on the design and implementation of a user-friendly, GUI-driven, open-source model of neocortical circuitry for the interpretation of non-invasively measured human brain activity. The neural model will be integrated into existing source localization software (MNE-Python) so that researchers can study the location, time course, and neural mechanisms of their human brain imaging data all in one package. Additionally, the post-docs will work with several test case groups to apply the model in a variety of tasks and clinical conditions. Interested candidates must have experience in Python programming and software engineering. The postdoc at Brown will work more on the computational modeling aspect of the software while the post doc at MGH will concentrate on the integration of MEG/EEG source modeling with computational neural modeling. Candidates with knowledge in computational modeling using NEURON and/or source localization methods will be given preference. Please send a CV, statement of research interests, and three letters of recommendation to contact addresses listed above.
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