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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
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- Delft University of Technology (TU Delft); 17 Oct ’25 published
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- Delft University of Technology (TU Delft); Published yesterday
- Delft University of Technology (TU Delft); today published
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the upcoming flood type, e.g. heavy-rainfall flood or rain-on-snow flood. As PhD candidate you will compare several machine-learning based algorithms regarding their ability to predict the flood type based
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/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a scenario generator for
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management of microgrids, battery scheduling, and handling uncertain renewable generation without relying on forecasted data [1]-[5]. Studies reveal that parametric tuning of RL algorithms such as state and
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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by: Developing specialized algorithms supported on solid theoretical foundations and with a focus on challenging aspects of very high-dimensional datasets, such as datasets encountered in
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algorithms that align prefab factory production schedules with IWT capacity, terminals, and urban delivery windows. Model multi-level planning decisions, connecting early feasibility assessment and quotation
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Plan programme, aimed as a flagship project for changing planning and scheduling in high mix low volume (HMLV) production by leveraging hybrid AI, data-driven workload estimation, intelligent release
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-to-end planning, connecting prefab factory production schedules with urban construction projects through an AI-driven decision-support system that synchronises production planning, IWT capacity, terminal
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industrial doctorate position is part of the Predictive Plan programme, aimed as a flagship project for changing planning and scheduling in high mix low volume (HMLV) production by leveraging hybrid AI, data
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to the research; Good understanding of computer architecture; Basic understanding of MRI algorithms is a plus; Understanding of AI and its practical implementations; The ability to work in a team and take