127 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" Postdoctoral research jobs at Rutgers University in United States
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capabilities necessary to communicate with other researchers and staff in person, on the telephone and by e-mail. Vision capable of viewing gauges, computer monitors, charts, forms, text and numbers
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capable of viewing gauges, computer monitors,charts, forms, text and numbers for prolonged periods. Must be available to work flexible hours, including overtime onshort notice and possible weekends and
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perform the job duties. Must be computer literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel and PowerPoint. Applicant should be highly motivated
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, nationality, marital or domestic partnership or civil union status, sex, pregnancy, gender identity or expression, disability status, liability for military service, protected veteran status, affectional
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, national origin, age, ancestry, nationality, marital or domestic partnership or civil union status, sex, pregnancy, gender identity or expression, disability status, liability for military service, protected
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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
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descriptions of current projects, please visit our lab website: https://sites.rutgers.edu/tina-liu-lab/ Under the direction of the Principal Investigator (PI), Dr. Tina Liu, the Postdoctoral Associate will
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, make discoveries, serve as an engine of economic growth, and generate ideas for improving people's lives. Connections working at Rutgers University More Jobs from This Employer https://main.hercjobs.org
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Abilities Strong quantitative and programming skills (e.g., MATLAB, Python, R). Solid background in signal processing, statistics, or computational neuroscience. Excellent written, verbal and computer skills
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to oversee research activities outlined in NSF Grant 2520154 “Understanding Expectation-Driven Learning in Early Childhood: An Experimental and Computational Investigation,” under the supervision of Dr. Kimele