98 postdoc-computational-fluid-dynamics-2017 Postdoctoral positions at Rutgers University
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. This postdoctoral associate position will be housed in the Department of Plant Biology at Rutgers University, New Jersey in Vaccinium breeding program of Dr. Gina Sideli. This position will also work with Rutgers and
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and international conferences. Supervise undergraduate students. Supervise graduate students. Minimum Education and Experience: PhD in biomechanics, kinesiology, biomedical engineering, computer
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renewable for two additional years pending productivity, performance, and overall fit. The postdoctoral program enables associates to build a high caliber scholarly portfolio in LGBTQ+ public health research
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and brain samples. Regular use of a computer is also required. Overview Statement Posting Details Special Instructions to Applicants Applicants must upload a cover letter, C.V., and the names and
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and gene-engineered T cells. Postdoctoral Associates are expected to establish an innovative, collaborative research program addressing important and fundamental questions. Active areas of research
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grant-funded research pertaining to the computational basis of reading and acquired reading impairments at the Rutgers site, under Dr. William Graves’ supervision. 1. Designing and implementing
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Position Details Position Information Recruitment/Posting Title Post-Doctoral Associate Department Engn- Ctr Adv Infrastructure Salary Details Min. Salary 63,968 Offer Information The final salary offer may be determined by several factors, including, but not limited to, the candidate’s...
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Transportation Center program. Position Status Full Time Posting Number 25FA0350 Posting Open Date 04/10/2025 Posting Close Date 01/05/2026 Qualifications Minimum Education and Experience Applicants must have a
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/benefits-overview . Posting Summary The Rutgers CEE Urban Informatics lab & The Infrastructure Resilience Group at Rutgers Center for Advanced Infrastructure and Transportation (CAIT) invite applications
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academic partners to bridge the gap between cutting-edge computational models and practical manufacturing workflows. The ideal candidate has a strong background in process modeling, control, optimization