102 channel-coding-electrical-engineering Postdoctoral positions at Rutgers University
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Qualifications Minimum Education and Experience PhD, MD, or equivalent doctoral degree in Neuroscience, Biomedical Engineering, Computer Science, or a related field. Certifications/Licenses Required Knowledge
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PowerPoint. Statistical coding skills (e.g., STATA, R) are also desirable. Preferred Qualifications Equipment Utilized Physical Demands and Work Environment Physical Demands: Standing, sitting, walking
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Qualifications Minimum Education and Experience PhD in Biomedical Engineering, Electrical Engineering, Materials Science, or a closely related field. Proven track record of research in flexible electronics
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projects Communicates regularly with the P.I. Maintains detailed and accurate experimental records. Supervises and trains subordinate staff. Reads current scientific and technical literature relevant
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Corporate Compliance Policy, Code of Conduct and Conflict of Interest Policy. Understands, demonstrates and models the Rutgers Cancer Institute core values. Keep abreast of all pertinent federal, state and
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for three references, and a link to a Github repository or other coding sample through our online application portal. Review of applications will begin on August or September 1 and will continue until
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aligned with the aims of the Addiction & Decision Neuroscience Lab (ADN). Current work focuses on cognitive modeling of decision-making in both laboratory tasks and real-world settings, as well as machine
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geophysical monitoring, data integration, and the development of predictive models and visualization tools. Candidates should have a PhD in geophysics, hydrology, environmental engineering, or a related
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molecular biology, immunology, biomedical engineering, or related field. Consideration to interview will also be given to applicants who are completing their PhD in molecular biology, immunology, biomedical
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research on characterizing non-coding regulatory elements in humans and understanding how these elements change across different conditions. The project will involve developing new modeling approaches