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Natural Science and the College of Engineering. CMSE centers around computational approaches, encompassing algorithm development, high-performance computing, scientific modeling, and data science
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Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials
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Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials
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-driven mechanical characterization of solid and porous materials, supported by an NSF CMMI CDS&E award and industry-sponsored projects. The primary function of the position is to develop and cross
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constitutive modeling of anisotropic and compressible materials. Minimum Qualifications Required PhD in mechanical engineering, aerospace engineering, materials science and engineering, civil engineering
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Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials
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interfaces and defects. This research will combine electronic structure methods, artificial intelligence, and data-driven techniques to understand and engineer novel materials for next-generation
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activities and data-driven predictive modeling for precision medicine. In addition to our core research questions and mission, the unit will also serve as a nexus between multiple branches and divisions and
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The Ohio State University | 2 months ago
analysis, machine learning, and data-driven modeling applied to large astronomical datasets and time-domain surveys is highly desirable. Although experience with microlensing and transit exoplanet
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materials characterization Strong background in computational materials science and machine learning applications Proficiency in programming and data analysis languages (LabVIEW, Python, MatLab, OriginPro