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on several NSF and NIH-funded projects focusing on metacognitive processes and self-regulated learning across learners, STEM, and biomedical learning tasks and domains, using serious games, simulations, and
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have expertise and experience in executing density functional theory (DFT) calculations, microkinetic modeling, kinetic Monte Carlo simulations, and Machine learning methods. Minimum Qualifications: Must
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Job Description This position offers a unique opportunity to work on advanced projects at the intersection of AI, machine learning, computer vision, and multimodal learning, focusing on advancing
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for energy yield prediction. - Develop models for performance loss rate analysis. - Conduct time series analysis and apply machine learning techniques to assess PV and energy storage system performance
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-level understanding of kinetics in solid-solid phase transitions; Develop advanced machine learning methods for the fast prediction of materials properties; Publish results in peer-reviewed journals and
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or engineering and have research expertise in optics, physics, chemistry, materials science, and spectroscopy. The Scholar will receive extensive technical and professional training and learn many
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communication and collaborative skills. Experience with SLAM, sensor fusion, LiDAR/depth camera data processing. Familiarity with deep learning for obstacle avoidance (e.g., map-less navigation). Background in
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innovation through state-of-the-art interdisciplinary research with social impact, contemporary inclusive teaching and learning practices, and preeminent service for the disciplines, the institution, and the
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independent postdoctoral fellows with training and expertise in agent-based modeling, neural networks, social network analysis, machine learning, data analytics, computational economics, or computational social