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is seeking a highly qualified, ambitious, and motivated PhD candidate for a position in bryophyte evolutionary biology. Bryophytes (mosses, liverworts and hornworts) were the first land plants
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the interface between different scientific disciplines including ecology, evolutionary biology, mathematics and statistics, informatics, economics and social sciences. We aim to apply advanced statistical and
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primary focus of the project, but complementary research on parallel systems may be developed. The project will add an important evolutionary component to ongoing interdisciplinary research on Arctic
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archaeology, and material culture from a long-term perspective. The Department of Natural History conducts research in systematics and taxonomy, evolutionary genomics, phylogeography, population genetics, and
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
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of structures, facilitating a form-finding process driven by FEM analysis. Training deep learning algorithms to suggest multiple structural concepts tailored to specific boundary conditions. Expanding FEM
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to your work duties after employment. Required selection criteria You must have a professionally relevant background in algorithms, machine learning, database systems, or data mining, with a research
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relevant background in algorithms and/or database systems with a research-oriented master’s thesis. Good programming skills. Good written and oral English language skills. Your education must correspond to a
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of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning
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selection criteria You must have a relevant background in algorithms and/or database systems with a research-oriented master’s thesis. Good programming skills. Good written and oral English language skills