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Field
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competencies Education You should have completed within the past five years or be close to completing a PhD in a relevant field such as data science, AI, computer science, machine learning, Earth system science
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nuclear physics detectors. Experience analyzing data from high energy or nuclear physics experiments. Familiarity with Monte Carlo simulations. Familiarity with machine learning techniques. About the
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, whether actual or perceived by others (including service-connected disabilities), gender (including pregnancy related conditions), military status or military obligations, sexual orientation, gender
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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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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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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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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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: 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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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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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