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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- University of Twente
- Wageningen University & Research
- Delft University of Technology (TU Delft); Published yesterday
- Delft University of Technology (TU Delft); today published
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Field
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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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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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-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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responsibilities include: Development of a flood classification framework for flood type prediction Comparison of different ML algorithms in a sensitivity study Communication with stakeholders Development of open
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to surveillance of infectious pathogens using computer science and mathematics? Join the Delft Bioinformatics Lab and work on graph-based algorithms for microbial genomics! Job description Bacterial and viral
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Do you want to contribute to surveillance of infectious pathogens using computer science and mathematics? Join the Delft Bioinformatics Lab and work on graph-based algorithms for microbial genomics
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skills and motivation to implement algorithms and test them in practice on large-scale problems. Programming Skills: You are proficient in at least one scientific programming language (such as Python
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this PhD project, you will investigate the co-design between event-based learning algorithms and neuronal hardware units with multi-scale time constants. The algorithmic methodology will exploit recent
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experimental testing. You’ll design and run experiments, write and train algorithms, and contribute to open-source tools that may one day become industry standards. This project offers the freedom to explore