72 linked-data-"https:" "https:" "https:" Postdoctoral positions at Stony Brook University
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associated with the NYB. Funding is provided by the New York State Department of Environmental Conservation (NYS DEC). More information about the New York Bight Indicators project can be found here: https
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associated with the NYB. Funding is provided by the New York State Department of Environmental Conservation (NYS DEC). More information about the New York Bight Indicators project can be found here: https
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Notes: Application Materials * curriculum vitae and personal website URL. * research statement. * two selected publications (links to publicly open URLs). * names of two references. * names of two
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or projects as assigned. Special Notes: Application Materials ● curriculum vitae and personal website URL. ● research statement. ● two selected publications (links to publicly open URLs
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or projects as assigned. Special Notes: Application Materials ● curriculum vitae and personal website URL. ● research statement. ● two selected publications (links to publicly open URLs
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), automated testing, and documentation for open-source research software. Preferred Qualifications: ● Experience with neuroimaging data modalities (e.g., EEG, fMRI) and familiarity with how empirical neural
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/26. Preferred Qualifications: Strong background in algorithms, human-computer interaction (HCI), and software engineering. Experience in robotics systems, interactive or intelligent systems, or AI
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software. Preferred Qualifications: * Experience with neuroimaging data modalities (e.g., EEG, fMRI) and familiarity with how empirical neural data can inform or validate computational circuit models
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/26. Preferred Qualifications: Strong background in algorithms, human-computer interaction (HCI), and software engineering. Experience in robotics systems, interactive or intelligent systems, or AI
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), automated testing, and documentation for open-source research software. Preferred Qualifications: ● Experience with neuroimaging data modalities (e.g., EEG, fMRI) and familiarity with how empirical neural