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 Cognitive Brain dynamics Lab (CBDL) is located at the National Brain Research Centre (NBRC) in Manesar, Gurgaon district. We are always looking for bright and motivated project assistants, Ph.D. students, and Postdocs. Please visit the NBRC web page to check relevant updates regarding doctoral admissions.

Postdoctoral positions will be hosted in NBRC under Cognitive Science Research Initiative/National Postdoctoral Fellowships 2017-18. If you are interested in applying please forward to me a CV and one-page research proposal.  

We are inviting application from Postdoctoral fellows who are interested in applying for Cognitive Science Research Initiative or any similar modes of funding. We are interested in supporting their application and would be happy to host them depending on mutual agreement. Some of the current projects in the lab are listed. Candidates with Ph.D. in Neuroscience, Cognitive Science, Biotechnology, Biomedical Engineering, Computer Science, Electrical Engineering, Physics, Mathematics, and Statistics are strongly encouraged to apply. Additionally, they must have a strong computational background with an interest and passion for Cognitive Neuroscience. Familiarity with statistical techniques, Signal processing, and knowledge of programming in Matlab/Python/C/C++  is essential.

  1. Applying whole brain large-scale Computational models developed to characterize normal versus pathological dynamical states (health vs. disease). This is a data-driven modeling project and requires programming skills, interest in systems neuroscience and cognition.

  2. EEG, Eye tracking data acquisition to understand the role of brain oscillations and large-scale brain network underlying perceptual learning.          
  3. EEG, fMRI data acquisition in resting state across different age groups to understand age-related cognitive decline. We develop computational methods to understand dynamic Functional connectivity and age-related reorganization. 

Initial inquiries should include a cover letter and current CV.  Please see contact details. 

We invite applications from talented undergraduate, graduate scholars. We seek individuals with passion and commitment for Cognitive Science/Neuroscience research for the project position funded by Department of Biotechnology (DBT). 
Any candidate with bachelor’s degree in the field of engineering in Computer Science/ Electronics/ Instrumentation or any related field like Biomedical engineering, Biotechnology OR holding a masters degree in Neuroscience/CognitiveScience/ Biotechnology/Physics/Mathematics/Statistics are encouraged to apply. Working knowledge of one of the programming languages Matlab/Python/C/C++ would be beneficial. We are actively recruiting  and training students in Brain Imaging, EEG, MEG recording and advanced data analysis methods applied to Cognitive Neuroscience:

Brief Ongoing Projects in the Lab 

  • Role of Default Mode brain network (DMN) in normal cognitive functions 

This multimodal approach using noninvasive human neuroimaging data recording from the brain using fMRI, EEG/MEG along with detailed large-scale computational models will establish links between neurotransmitter imbalance in the organization of large-scale anticorrelated neural systems, cognition, and symptoms (broadly speaking cognitive impairment) associated with impaired cognition in humans. This study will be carried out in National Brain Research Center involving fMRI data acquisition primarily in the resting state and simple tasks from participants over a range of age groups. EEG/MEG component of the data acquisition will also take place at NBRC.   

  •  Role of Subcortical brain areas in modulating large-scale functional and effective connectivity in resting and Cognitive tasks in aging and mental health 

This project requires setting up behavioral experiments, EEG acquisition, and analysis. A possibility of collecting behavioral data inside the scanner. We have 64 channel EEG recording facility along with two different Eye tracking systems. This study uses also big data repositories such CAMCAN, ABIDE etc. for building a large-scale computational model to explore the computational mechanisms underlying cortical-subcortical interactions. 


  • Image processing and statistical models to understand fMRI, EEG/MEG dynamics 

For example, an in-depth analysis of statistical issues involved in studying fMRI dynamics revealed surprising results, e.g., spectral graph theoretic and graph diffusion models explain fMRI dynamics and capture individual subject variability better than nonlinear biophysical models (Surampudi et al., 2017; Surampudi et al., 2018). While the previous examples are specific to neuroimaging, we have also developed general methods applicable beyond neuroimaging to characterize EEG/MEG source and sensor level dynamics which may find diverse applications. 

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