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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
reduction and manifold learning * High-dimensional inference and feature selection * Generative modeling and digital twins * Reliability and interpretability of ML methods The postdoctoral researcher will be
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, Mathematics, or closely related fields. • Experience conducting experimental, theoretical, or computational research. • Familiarity with mathematical modeling, data analysis techniques, or computational tools
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of nonlinear PDEs or the development of new solver- or coupling-methods incl. their convergence analysis, but also modeling and simulation aspects across a wide range of fields - from biomechanics and geophysics
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: Be'er Sheva, HaDarom 8410501, Israel [map ] Subject Areas: Physics / HEP-Theory (hep-th) , hep-th , Field Theory , High Energy Physics , High Energy Theory , Mathematical Physics , Physics , Physics
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Science Statistics / Biostatistics Applied Mathematics Data Science Demonstrated expertise in modern machine learning, including at least one of the following: Deep learning (e.g., transformers, sequence models
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are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab
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on the development of technology for lunar and Martian regolith handling and beneficiation. · Develop and apply modelling and simulation approaches to particle separation systems. · Optimize the design
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field Strong foundation in electrochemistry, electrochemical engineering, and chemical processing Demonstrated experience in mathematical modeling of electrochemical systems; knowledge of solid mechanics
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. These applications include, but are not limited to, anomaly detection, modulation classification, sensing, and adaptive spectrum optimization in diverse operational environments. Your work will focus on modeling and
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. Mathematically, a network is represented by a graph, which is a collection of nodes that are connected to each other by edges. The nodes represent the objects of the network and the edges represent relationships