Current Job opening
Brain Connectivity, Cognition and dynamics Lab (BCDL) is located at Indian Institute of Technology, Jodhpur and also at National Brain Research Centre (NBRC) in Manesar. IIT Jodhpur is now our lab’s primary location. We encourage you to apply here Centre of Excellence in Brain Science & Applications housed in the School of Artificial Intelligence and Data Science (SAIDE). We are looking for motivated project assistants, Ph.D. students, and Postdocs with interest and willingness to solve fundamental brain research problems and also develop new data driven algorithms/methods for understanding Neuroimaging data. Please visit IIT Jodhpur School of AIDE web page to check relevant updates regarding doctoral admissions.
We are inviting application from Postdoctoral fellows who are interested in applying for National level support for hosting postdocs or any similar modes of funding. We are interested in supporting their application and would be happy to host them depending on mutual agreement.Postdoctoral positions will be hosted at IIT Jodhpur School of AIDE under Cognitive Science Research Initiative (CSRI) / National Postdoctoral Fellowships (NPDF)/DBT-Welcome early career schemes 2022-23. If you are interested in applying please forward me your CV and one-page research proposal.
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.
Applying whole brain large-scale Computational models/Machine learning/Deep learning models and algorithms to characterize normal versus pathological dynamical states (health vs. disease), memory and perceptual processing, Multisensory speech processing, aging brain dynamics and brain networks classification in ASD, ADHD. This is a data-driven modeling project and requires programming skills, interest in systems neuroscience and cognition.
- EEG, Eye tracking data acquisition to understand the role of brain oscillations and large-scale brain network underlying perceptual learning, decision making and executive functioning.
EEG, fMRI data acquisition in resting state, working and episodic memory across different age groups to understand age-assocaited neural correlates of cognitive decline. We develop computational methods using nonlinear dynamics, signal processing, graph theory to understand dynamic functional connectivity, cognitive and neural flexibility and age-associated reorganization.
Initial inquiries should include a cover letter and current CV. Please see contact details.
Brief Ongoing Projects in the Lab
Lifespan associated alterations in large-scale brain network dynamics and 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 Ageing 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 (Shastry et al., 2022; Pathak et al., 2022; Thuwal et al., 2021; Das et al., 2021; Shaoo et al., 2020).
Image processing, deep learning, data fusion 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 (Ghosh et al., 2021; Kumar et al., 2020; Surampudi et al., 2019; 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.
transcranial neurostimulation, neurofeedback to understand causal nature of EEG Brain connectivity during memory, attention, perception and multisensory processing
Brain state dependent stimulations, feedback and modulation of neurophysiological responses and oscillatory changes. For example, does the regulation of local excitation-inhibition balance aid in recovery of functional connectivity and change in endogenous brain states to alter perceptual variability and sensitivity during perception, memory and attentional processing (Roy et al., 2021; Naskar et al., 2021; Vattikonda et al., 2016; Dagar et al., 2016).