118 parallel-computing-numerical-methods-"DTU" Postdoctoral positions at Rutgers University
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reinforced concrete structures, using mechanical, acoustic, electromagnetic, electrical, and electrochemical methods and devices, such as ultrasonic methods or GPR, 2) application of numerical simulation and
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of statistical concepts and methods. Proficiency in at least one programing language (e.g., R, SAS, STATA). Excellent quantitative analytical skills, particularly in longitudinal modeling, administrative data
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neuroscience research with clinical applications to addiction and other mental health concerns. Our work draws on methods from psychology, experimental economics, human neuroscience, and computational psychiatry
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he successful candidate is expected to have experience with advanced functional neuroimaging data analysis methods and/or computational cognitive modeling. Preferred Qualifications Previous experience
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policy analysis approaches and methods, including stakeholder interest and conflict analysis, benefit-cost analysis, utility rate development, program evaluation, regulatory policy analysis and report
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to work independently; • Experience working on research projects with a research team; and • Excellent verbal, writing and analytical, organizational and attention to detail skills, computer literacy and
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manages data collection, cleaning, and analysis for assigned projects. Manages various data projects (including data validation, cleaning, and transforming program administrative data), and collecting and
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span a broad range of areas including strongly correlated fermion materials, high-temperature superconductivity, topological electronic states of matter, developments and applications of computational
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-infiltrating lymphocytes and gene-engineered T cells. Postdoctoral Associates are expected to establish an innovative, collaborative research program addressing important and fundamental questions. Active areas
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lead research projects to investigate how bioactive lipids and other related bioactive compounds influence chronic diseases using advanced analytical techniques and statistical methods under Dr. Liang’s