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on applying computer vision, machine learning, and sensor fusion to automatically detect, classify, and localize defects, improving the scalability and reliability of building inspection. Research on 3D
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. Experience in molecular modelling, simulations, AI, and machine learning applied to proteins. A track record of research outputs, including publications and presentations at national or international level
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multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine learning) to combine datasets and preserve extremes. Uncertainty will be quantified explicitly, with outputs
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background in AI—such as knowledge of machine learning or neural networks—will be an advantage. The appointee is expected to conduct focused research, publish scholarly outputs in reputable, peer-reviewed
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background in AI—such as knowledge of machine learning or neural networks—will be an advantage. The appointee is expected to conduct focused research, publish scholarly outputs in reputable, peer-reviewed
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Position Overview School / Campus / College: College of Engineering Organization: Electrical and Computer Engineering Title: Research Assistant Professor (Non-Tenure) - Li Lab Position Details
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sponsorship. Updated on 11/05/2025. Qualifications Required Qualifications PhD in aerospace engineering or related field Two or more years of research training Excellent analytical and computer programming
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or if you have questions about a job posting, please contact Human Resources at 479.575.5351. Department: Department of Physics Research Assistants 04 Department's Website: https://physics.uark.edu/ Summary