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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); yesterday published
- University of Amsterdam (UvA)
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- Delft University of Technology (TU Delft); Published 11 Nov ’25
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- Eindhoven University of Technology (TU/e); Published yesterday
- Eindhoven University of Technology (TU/e); today published
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- Erasmus MC (University Medical Center Rotterdam); today published
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- Leiden University Medical Center (LUMC); yesterday published
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- Maastricht University (UM); 16 Oct ’25 published
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- University of Amsterdam (UvA); 26 Sep ’25 published
- University of Amsterdam (UvA); Published today
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of Science at UvA. What are you going to do? The aim of the project is to use advanced Machine Learning techniques to predict the anharmonic vibrational spectra of large Polycyclic Aromatic Hydrocarbon (PAH
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machine learning to facilitate crop breeding by design. This project envisions to build a system that enables precise introgression of desirable traits into elite crop varieties by predicting recombination
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and biomedical materials. Growing focus on the use of artificial intelligence techniques to support and accelerate the experimental work, e.g. data interpretation and prediction of structure-property
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perovskites — for applications in sustainable energy, spintronics, and quantum technologies. By combining physics-based theory with data-driven models, you will contribute to the next generation of predictive
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can focus on learning for planning, risk-aware motion planning under uncertainty, learning of interaction models, multi-robot learning, multi-modal prediction models, or other related topics
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durability against chloride ingress and carbonation; Predicting service life of precast SCC elements; Coupling experimental durability data with advanced numerical simulations. The researcher will be based in
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) incorporating mineralized SCMs. The research will focus on: Modeling long-term durability against chloride ingress and carbonation; Predicting service life of precast SCC elements; Coupling experimental
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introgression of desirable traits into elite crop varieties by predicting recombination landscapes across a vast number of potential parental crosses. Implementing the project involves working with a variety of
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, Interns and Visiting Researchers, as applicable; develop and evaluate AI/ML models to identify, quantify and predict climate change impacts relevant to adaptation, resilience and mitigation on the topics
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learning, statistical techniques, and AI to analyze data, predict response to diet, and identify signatures determining response to diet. A strong foundation or affinity with statistical modeling, with