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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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assessments: both predictions and reference data change over time, requiring methods that account for temporal misalignment. Variable observation density: seasonal cloud cover, satellite revisit times, and
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systems increasingly provide personalized recommendations in domains such as nutrition and lifestyle. However, many recommender and prediction systems rely heavily on opaque machine learning techniques
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of interest to you. PhD position combining high dimensional data for the genomic prediction of methane emissions. Wageningen University & Research’s Animal Breeding and Genomics group leads the Global Methane
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to predict yield. Write and publish your research in leading scientific journals and communicate your findings through your PhD thesis and broader societal outreach. You will work here This research is