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(ChemE) invite applications for a Postdoctoral Researcher position in the labs of Dr. Meagan Mauter and Dr. William Tarpeh. This interdisciplinary position will focus on the development of process modeling
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NHANES and related datasets to identify patterns linked to oral health outcomes and health disparities. Apply and develop bioinformatic pipelines, statistical models, and computational tools to assess
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composites and develop science; and engineering methods for designing, scaling, optimizing and controlling relevant manufacturing processes. These positions will be based at the Rutgers’ School of Engineering
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modeling, LLM and/or NLP, behavioral coding, and/or psychophysiological monitoring. For consideration, please click the link below to apply and submit all required application materials: https
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proficiency in modeling to assess how post-wildfire impacts soil functioning (infiltration, runoff, strength, composition, ecology), smoke and pyrometeor deposition, soil carbon and nutrient cycle alterations
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-world court data on racial composition of juries and videos of jury selection, databases of real 911 calls, psychophysiological data during large-group decision making) on topics that could include (but
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
models, and translational assays in clinical coagulation. Cancer biology and blood biology are inseparably linked. Cancer increases the risk of bleeding and inappropriate blood clots (thrombosis), both
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dependent educational benefits Life insurance coverage Employee discounts programs For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits/benefits-overview
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RSM-based experimental design and regression modeling. Strong record of peer-reviewed publications to the field of advanced cementitious composites and nanocomposites. Ability to work collaboratively
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heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites