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Carl von Ossietzky Universität Oldenburg | Oldenburg Oldenburg, Niedersachsen | Germany | 30 days ago
develop and refine novel mobile neurotechnology, such as ear-centered EEG (ear-EEG) and the nEEGlace (an EEG necklace), which enable us to record brain activity during daily activities. Our research has a
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multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a team with a strong track record in Big Data analytics in child mental health, helping accelerate the move toward
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develops and analyses behavioural experiments (reaction times, eye fixations) and EEG data. The computational strand simulates behavioural and neural data related to processing and memory retrieval. As a
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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Investigator Dr. Christopher Petkov, the Postdoc joins a team leading on these unique dataset acquisition and advanced analytics. Testing will include site-specific single neuron and laminar recordings, fMRI/EEG
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using high-density electroencephalography (EEG). The candidate will be expected to work collaboratively with a research team to collect and analyze data, mentor junior lab members, prepare manuscripts
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of neurological disorders and human cognitive experiments combining EEG in the lab will further enhance these findings, and the applicant will have a chance to collaborate on these projects. About You Strong
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to the design, analysis, and interpretation of data using neuroimaging (EEG/MRI), behavioural, and/or computational approaches prepare manuscripts, reports, and presentations to disseminate research findings
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(7T fMRI, MR Spectroscopy), electrophysiology (EEG), interventional (TMS, tDCS) and neurocomputational (machine learning, reinforcement learning) approaches to understand the network dynamics