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University of Veterinary Medicine Hannover • | Hannover, Niedersachsen | Germany | about 18 hours ago
must be broad in both content and methodology in order to acquaint students with the many aspects of systems neuroscience, while the emphasis must remain specifically on basic knowledge. Excessively
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, Radioecology, Mineralogy, Geology or related field Experience in experimental and analytical work and in the evaluation of extensive analytical data sets Knowledge of handling radioactive substances is desirable
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to theorizing about the evolution of common ground, one of the central research topics of the Collaborative Research Center in which this project is embedded. We will explore the implied cognitive complexity
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(social sciences, political science, economics etc.) Desirable: - good knowledge of quantitative tools of empirical social science research. - experience in academic work; - knolwegde of university
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) and should be used for a doctorate. Participation in the accompanying doctoral program is compulsory. This serves to impart both scientific and methodological knowledge and offers the opportunity
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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Completed university studies (Master/Diploma) in the field of Chemistry, Radiochemistry, Radiopharmacy, Radioecology or related field Excellent knowledge of a broad range of organic chemistry and analytical
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for diversity Desirable knowledge and skills include: o Understanding of the principles that define DNA, RNA, protein structures, functions, dynamics and interactions o Experience in plant work
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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic