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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 23 hours ago
. This position offers the opportunity to engage in groundbreaking research in separation processes using clean energy sources. Minimum Education and Experience Requirements PhD in Chemical Engineering, Mechanical
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, data mining, machine learning, natural language processing, human-centered computing, mobile and ubiquitous computing, cyber-physical systems, and engineering education. IST hosts the ABET-accredited BS
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to machine learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You
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Biology, Machine Learning, Bioinformatics, Biotechnology or related discipline Experience with computational tools for protein engineering or machine learning Demonstrated ability in biological data
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design and discovery, including the use of artificial intelligence (AI) and machine learning (ML) techniques. The hired candidate will focus on computational aspects of immune repertoire analyses
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and
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or a strong aptitude for using advanced computational tools, AI, or machine learning techniques to address engineering challenges; and interest or initial experience in interdisciplinary collaboration
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journals or conferences; experience with or a strong aptitude for using advanced computational tools, AI, or machine learning techniques to address engineering challenges; and interest or initial experience
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experience in electrochemistry, or environmental engineering and water technology. Experience with multivariate data analysis, statistics, machine learning, numerical simulations, and programming. Skills in
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recognition, and enable seamless collaboration between humans and machines. Long-Term Human-Technology Evolution: investigate the longitudinal impact of human-technology interaction on learning, behavior, and