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are not designed to produce reliable regional estimates of those phenomena. Therefore, small area estimation (SAE) methods are used. With technological advances, Big Data now offers valuable spatial
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. Dr Chisari’s team engages in major large-scale structure surveys (e.g. LSST DESC, Euclid), and visits to Leiden Observatory to collaborate with the lensing group are planned. Involvement in outreach
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physical models. However, to achieve reliable results choosing the right methodology and training strategy is a large scientific challenge. Your job In this project, we aim to apply deep learning techniques
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the climate system and have all been identified as large scale tipping elements, albeit on very different time scales. While for each of these tipping elements critical thresholds remain matter of active
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innovative research on marine mammal health and mortality; analysing large datasets from post-mortem investigated marine mammals and related environmental factors; applying advanced data analysis techniques
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. You have a background in machine learning for spatial data (e.g., random forest, neural networks) or are open acquiring these skills. You have experience with handling large geospatial datasets and
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as intellectually challenging as it is relevant! Your job Large volumes of green power and green hydrogen are needed to green industry and the economy as a whole. A major uncertainty concerns the
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of the interventions, as they are team efforts carried out by a large team of scholars and curators. You will be primarily responsible for incorporating a critical heritage and museology perspective. You will assist in
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potential large-scale climate repercussions. Even more so since the AMOC brings CO2 from the surface to the deep ocean during deepwater formation (physical pump), and variations in the AMOC strength will
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been protected from storms by large storm surge barriers such as the Oosterschelde and the Maeslant barriers. But: what is the role of these barriers in the future? As a PhD candidate in this project