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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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. Forbes also named it ‘best city in the U.S. for business and careers’ and the #10 most educated city in the U.S., and Southern Living named Durham ‘The South’s tastiest town’. To learn more, visit: http
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) Demonstrable experience/training in the concurrent use of fMRI and EEG Demonstrable experience/training on the use of phenomenological measures Experience with Machine Learning methods to assess aspects
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HCI and cybersecurity, to cancer research tools and methods for numerical analysis and machine learning. The research work takes place in a multidisciplinary team with a focus on image processing with
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statistical and machine learning techniques to analyze datasets related to aging, Alzheimer’s disease or related dementias. The successful candidate will work as part of a multi-disciplinary team including
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development in large multi-site cohorts; and 3) developing advanced machine learning models of task-related brain function. The fellow will have the opportunity to contribute to these projects and will also be
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opportunity to advance the integration of machine learning with multimodal biological data (including genomics, neuroimaging, digital phenotyping, and clinical information) to address foundational questions in
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to be able to independently operate the 3T & 7T scanner consoles Learn and train others on the CNS Lab developmental neuroimaging standard operating procedures Assist with the collection of MRI data (may
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parcellation (Glasser et al., 2016 Nature). The post-doc will be co-mentored by Matthew F. Glasser MD/PhD and David C. Van Essen PhD and be based in the Glasser/Van Essen laboratory in the WashU Radiology
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to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping to build healthier, more