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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
prediction using large-scale multimodal neuroimaging data. The research will emphasize methodological innovation in statistical modeling, transfer learning, machine learning, model ensemble strategies, and
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and as part of a collaborative, interdisciplinary team. Commitment to publishing research and pursuing a career in academic or translational research. Experience with statistical modeling, machine
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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, 2025. Preferred Qualifications: Experiences in electromagnetic transient analysis, grid-forming IBR modeling, quantum computing, power system stability, cybersecurity, real-time digital simulation, and
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security. Expertise in data-driven modeling (ML for energy, forecasting, anomaly detection) and physics-informed learning. Real-time/HIL or embedded control experience (e.g., OPAL-RT, RTDS) and laboratory
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, behavioral, and mindfulness-based treatments for anxiety; and modeling who benefits most from learning many emotion regulation skills or a single emotion regulation strength. The Post Doctoral Scholar will
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, creates tools for synthetic observations, accelerates forward modeling using machine learning, and couples physics-based models with simulation-based inference techniques. Our goal: create a robust
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the opportunity to lead manuscript and grant writing and submit publications as the primary author. Responsibilities: Effectively design, implement, and evaluate machine learning and computational
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include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work in applications
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches