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long-generation organisms where it is harder to study. Genomic prediction is a powerful tool for breeding, and it is known in crop plants that plastic variation is heritable. With genomic prediction
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this, little is known about the genetics underlying plastic responses, especially in long-generation organisms where it is harder to study. Genomic prediction is a powerful tool for breeding, and it is known in
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with unknown but presumably large prediction uncertainties, their broad application is hindered. This is especially a problem in application areas that require robust and trustworthy solutions, such as in
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enablers for predicting and reasoning about dynamics and transport patterns, in turn being of key importance for a broad range of human activities. The research in the PhD project will focus on core spatio
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, to thermoelectric and electronic transport measurements. The project also incorporates a theoretical component, including theoretical predictions of material stability and properties, carried out in collaboration
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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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more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/ Genetic variation and genomic prediction in farm animals Research subject Animal Science
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genomic prediction in farm animals Research subject Animal Science Description: We are looking for a motivated and committed PhD student who wants to understand the genetic consequences of modern breeding
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on understanding how conifers adapt to new environments by developing genetics-based models for predicting future tree performance under changing climates. For more information about the departments, visit
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developing genetics-based models for predicting future tree performance under changing climates. For more information about the departments, visit: Department of Forest Biomaterials and Technology | slu.se