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imaging, mathematical modelling, and functional genomics, receiving experimentally testable predictions generated by state-of-the-art predictive models. These predictions will be rigorously validated using
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predictive models for evaluation of the role of dietary in health and disease and establish personalized dietary strategies for more effective disease prevention. In many cases, the work involves time series
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strategic raw material (CRM) deposits within Paleoproterozoic cover sequences that overlie an Archaean basement. Existing structural models for the cover rocks predict that crustal-scale fault and shear zone
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Paleoproterozoic cover sequences that overlie an Archaean basement. Existing structural models for the cover rocks predict that crustal-scale fault and shear zone systems extend into the basement and that structural
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, better adapted individuals can be selected at the seedling stage using only genetic data, accelerating the breeding cycle. Incorporating information about plasticity can aid genomic prediction modeling
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research in data-driven nutrition, health, and food science. With large-scale diet and health data, omics data, biomarkers, digital food and health services, we establish predictive models for evaluation
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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and predict the sensitivity and resilience of Swedish conifer forests to interacting soil fertility and droughts. Specifically, we want to detect whether long-term fertilization practices have modified
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will generate synthetic data from personal data to mitigate scarcity in rare diseases. This will facilitate data availability for further development of predictive models, thereby enhancing the quality
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models also provide uncertainty estimates in the model and its predictions, allowing the confidence in automated decisions to be evaluated. The goal of this project is therefore to develop learning