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Field
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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implement innovative architectures for real-time detection and control of laser processes. This interdisciplinary role combines artificial intelligence and machine learning with the physics of laser–matter
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industrial conditions. Keywords: Artificial intelligence, autonomy, digital twin, edge computing, UAV systems. Objectives: Support the development of AI and machine learning algorithms for autonomous
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methods that operate under realistic service conditions. This postdoctoral position is part of PolyMIND (AI-enabled Polymer monitoring via Multi-sensor Intelligent Non-destructive Data fusion), the funded
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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quality alarm protocols based on machine learning: thresholds, alert workflows, and response or shutdown measur. Analyze data (time series) and develop quality indicators to support municipal decision
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 4 hours ago
, or similar platforms for thermal and fluid dynamics simulations. Strong programming skills in MATLAB, Python, R, or similar. Machine learning or data-driven modeling experience applied to environmental control
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: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration
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sustainability of biomaterial manufacturing through safe design methods, machine learning, and predictive life cycle assessment as well as developing machine learning and hybrid digital modeling methods, combining