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Field
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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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(DSO) to resolve local congestion issues. The design of such an edge decision-making platform requires an understanding of distribution systems operation as well as machine learning and optimization
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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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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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), gender identity or expression, genetic information, HIV/AIDs status, military status, national origin, pregnancy (false pregnancy, termination of pregnancy, childbirth, recovery therefrom or related
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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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(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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: 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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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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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