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Field
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behaviour and provides active personalised learning for improved instant decision making. Key beneficiaries are expected to be construction industry stakeholders, for example, project owners, architects, engi
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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 opportunities to present at leading
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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, compression, learning, and inference for classical and quantum data. The stipends are within the general study programme Electrical and Electronic Engineering or Wireless Communications, and available from
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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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(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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: 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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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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are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure