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Field
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) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled
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: excellent, very good or good university degree (diploma, master's degree) in transport or related study programs with a solid basis in transport planning and/or data analytics Description of the PhD topic
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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researchers in soft robotics, control theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine
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be completed within four years. You are also requested to teach. You will work with the following supervisory team: - Dr. Frank Harbers (co-promoter) - Dr. Rik Smit (co-promoter) - Dr. Scott A
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, conference presentations, and ultimately a PhD dissertation. The PhD thesis is to be completed within four years. You are also requested to teach. You will work with the following supervisory team: Dr. Frank
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the hiring
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success