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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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of physical systems AI-based condition monitoring Reinforcement learning Programming skills are required, with Python experience preferred. Theoretical understanding and hands-on experience with electric
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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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a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the
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mathematics or a related field. The successful candidate will have expertise in at least in one of: Machine learning in the context of physical systems AI-based condition monitoring Reinforcement learning
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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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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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(PyTorch, Tensorflow etc.) Knowledge of multi-agent systems and autonomous agent modelling Expertise in Machine Learning and Artificial Intelligence We consider the following as an advantage: Willingness
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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