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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 2 hours ago
optimization algorithms and/or machine learning models for complex systems or real-world applications Experience with large-scale optimization or deep learning systems is highly desirable Duties and
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learning-assisted computational pipeline for the automated detection of point defects in atomic-resolution scanning transmission electron microscopy (STEM) images. Using monolayer MoS₂ as a model system, the
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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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satellite imagery are fused with land-surface models using data assimilation. The goal is to develop an adaptive experimental design frame-work for the observing system to guide ongoing measurement campaigns
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organizations with varying technical capacity. Explore applications of AI methods, including large language models and natural language processing, for extracting structured information from unstructured sources
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will be required to provide a mass value and model-based uncertainty using multiple methods to cross-check. These methods may include mass spectrometry and inertial balance development and prototype
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with animal models, including animal handling and the application of relevant ethical and regulatory requirements. About UQ As part of the UQ community, you will have the opportunity to work alongside
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on the seismic performance of structural components. - Develop consequence models for various structural systems to demonstrate the real-world application of the unified database. - Design and
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Number: 1142862 Position Summary We are looking for creative, motivated, and passionate scientists to join our growing team. We welcome researchers who fit into one of the following tracks. Please note
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., hyperspectral) and climate reanalysis data, calibrated with in situ field data, laboratory analysis, and numerical models of landscape evolution. Apply today to spearhead groundbreaking landscape evolution