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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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to develop a holistic e-motor cooling technology, maximising heat transfer through direct-contact, spray cooling. The Team is looking to appoint one Postdoctoral Research Associate on Machine-Learning Assisted
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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