Please find below descriptions of two available postdoctoral positions

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Applications are invited for a postdoctoral position using multivariate, machine learning methods to study the neural basis of high level vision, with an emphasis on face recognition and social and affective perception, at the University of Pittsburgh, USA.

The research will focus on understanding the dynamic neural code that underlies the recognition of faces, bodies, facial expressions, and other social and affective stimuli. Of particular interest is how this information is coded in interactive neural circuits at the level of large-scale brain networks. The neural data will primarily be local field potentials/ event related potential from surface electrodes (electrocorticography, ECoG), cortical depth electrodes, and subcortical depth electrodes in humans during presurgical procedures for the treatment of epilepsy. Multivariate classification techniques will be applied to the millisecond-scale activity recorded simultaneously from multiple electrodes in areas related to social processing while participants perform social and affective perception tasks.

Applicants should hold a Ph.D. in Neuroscience, Psychology, Computer Science, Mathematics, Statistics, Engineering or a related field. Advanced skills in signal processing, multivariate statistics, machine learning and/or computational neuroscience are required. Experience with multivariate analysis of neural data is required and experience with electrophysiological data is desirable. Applicants should have a strong track record of publication.

The position is fully funded. The initial period is 1 year with additional years depending on fit and progress. Interested applicants should send a letter of interest, a CV, and the names and contact information for 3 researchers who can provide a recommendation. For further information or to submit an application, please contact Avniel Ghuman, Ph.D. at [log in to unmask] .

The position is available immediately and applications will be considered until the position is filled.



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Applications are invited for a postdoctoral position to support a collaboration between researchers at Carnegie Mellon University and the University of Pittsburgh, USA to develop new methods to combine multimodal neuroimaging data to understand brain networks.

The research will focus on the development of new tools to combine high dimensional MEG (magnetoencephalography), fMRI, and structural MRI data to discover brain network structure. This will include developing algorithms to understand similarities between data types (neurovascular relationships, structure-function relationships, etc.) as well as discovering what unique information each modality provides. These tools will be developed with the purpose of uncovering how brain networks are disturbed in existing multimodal data from individuals with autism spectrum disorder and schizophrenia. Furthermore, these tools will be developed with aim of applying it to large pubic neuroimaging datasets, such as those being released as part of the Human Connectome Project.

Applicants should hold a Ph.D. in Neuroscience, Computer Science, Mathematics, Statistics, Engineering or a related field. Advanced skills in multivariate statistics, missing data problems, graph theory, dimensionality reduction, network analysis, machine learning and/or computational neuroscience are required. Experience with analysis of human neuroimaging data is desirable. Applicants should have a strong track record of publication.

The position is fully funded. The initial period is 1 year with additional years depending on fit and progress. Interested applicants should send a letter of interest, a CV, and the names and contact information for 3 researchers who can provide a recommendation. For further information or to submit an application, please contact Avniel Ghuman, Ph.D. at [log in to unmask] or Aarti Singh at [log in to unmask] .

The position is available immediately and applications will be considered until the position is filled.