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(https://aihub.osu.edu ). The AI(X) Hub at The Ohio State University is a university-wide initiative to accelerate research, innovation, and education in artificial intelligence. It spans 15 colleges and
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, evolutionary game theory, infectious disease modeling, and assistive and accessibility technologies, with a shared commitment to improving health outcomes through data-driven discovery. Dr. Strings lab profile
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patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
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experience with process modeling with Aspen Plus Demonstrated experience with statistical analysis (i.e. sensitivity or uncertainty) PhD degree in chemical engineering or related field Preferred Qualifications
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of Time • Conduct experimental research, including characterizing mechanisms of plastic and e-waste degradation, genetic modification and engineering of model and non-model microorganisms, designing
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Postdoctoral Research Fellow with expertise in large language models (LLMs) and electronic phenotyping to join our dynamic team focused on advancing cancer research through innovative data-driven approaches in
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intersection of solid mechanics, constitutive theory, and data-driven modeling, while contributing to fundamental advances in soft material mechanics and developing transferable skills applicable to a broad
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modification and engineering of model and non-model microorganisms, designing, troubleshooting, and optimizing experiments, collaborating with other lab members to support complementary projects, participating
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, particularly relating to groundwater and/or stormwater, organic contaminants, and engineered adsorbents. One or more years of experience with advanced data analysis and/or environmental modeling techniques
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hydrologic and hydraulic models (e.g., WRF-Hydro, HEC-RAS, OpenFOAM, GSSHA, Delft3D, EFDC, etc.). Data Engineering & Computational Workflows – 35% Curate, preprocess, and analyze large environmental datasets