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written communication skills. Must be computer literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel, Access, and PowerPoint. Preferred Qualifications
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stimulation and recording electrodes. Design, execute, and analyze research studies using intracranial EEG (ECoG, SEEG) data from animals and humans. Perform advanced signal processing and time–frequency
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of Oceanography, and may also contribute to a separate project funded by the National Science Foundation that involves process mechanics in marine boundary layer clouds. The successful applicant must possess a Ph.D
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materials chemistry and surface science, with a focus on the development, operation, and analysis of specialized physical characterization techniques. Position Status Full Time Posting Number 26FA0156 Posting
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literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel, Access, and PowerPoint. Preferred Qualifications Prior research experience preferred. Basic
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advanced signal processing and time–frequency analyses of neural recordings. Develop and implement computational pipelines using MATLAB, Python, or similar platforms. Lead manuscript preparation for peer
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proficiency in MS-office applications required. Requires knowledge of Microsoft office and other computer related skills. Command of the English language must be at a skill level necessary to produce materials
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, NJ). Performs data management and processing workflows. Maintains research image archives. Generates orthomosaics. Organizes and backs up large geospatial datasets. Operates software pipelines
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include the following: Conduct research in the area of aging, balance, fall rehabilitation, app development, and biomechanics. Data Post-processing. Programming using MATLAB, Python, and Java/Kotlin
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this position. WORK ENVIRONMENT: The candidate will work primarily in Newark, NJ and/or New Brunswick, NJ with some remote flexibility. Candidate will have access to a standard computer, phone, and internet