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Bayesian belief networks; Experience in scenario development approaches, e.g. SSPs; Experience in the application of R-based analytical tools for qualitative or semi-quantitative modelling, incl. RQDA
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”, “Biodiversity Genomics”, and “Solution Labs”. Objectives include the transformation of natural history collections and data bases into the digital era, studying biodiversity from the genome to the ecosystem
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, ocean, marine ecosystem, and impact models of different complexity and will include both traditional and new ocean modelling approaches with the final objective of delivering: (i) coordinated and
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traditional and new ocean modelling approaches with the final objective of delivering: (i) coordinated and harmonized multi-model datasets of high-resolution decadal and multi-decadal scenario simulations
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created. At the same time, one of the world's most comprehensive natural history collections with over 30 million objects will be housed in modern collection buildings and completely digitized in
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, focusing on regional ocean climate predictions and projections. The primary objective of this doctoral student position is to disentangle natural climate variability and projected anthropogenic climate
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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge
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created. At the same time, one of the world's most comprehensive natural history collections with over 30 million objects will be housed in modern collection buildings and completely digitized in