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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
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- Eindhoven University of Technology (TU/e)
- Maastricht University (UM); yesterday published
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- Eindhoven University of Technology (TU/e); Eindhoven
- Erasmus MC (University Medical Center Rotterdam)
- Erasmus MC (University Medical Center Rotterdam); today published
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- University of Amsterdam (UvA); 26 Sep ’25 published
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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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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
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the execution of the dietary intervention trials Ability to apply machine learning, statistical techniques, and AI to analyze data, predict response to diet, and identify signatures determining response
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Sciences and Biotechnology Institute (GBB) of the University of Groningen (The Netherlands), to investigate RNA structural ensemble dynamics in living cells. What The successful applicant will work on a
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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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heavy emphasis on data-driven methods, for understanding content, for analyzing and predicting user behavior, and for make sense of context. We combine fundamental, experimental, and applied research, and
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prediction of BESS’s electric and thermal behaviours. Optimization of BESS design for high energy density, durability and safety. Validation of models by benchmarking with cell and system level measurements
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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