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The postdoctoral fellow will work on fundamental aspects of Arctic Ocean circulation, using theory, modelling, and observations. The understanding of the circulation of the intermediate Atlantic Water and the deep
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results from such experiments, is required, as is deep knowledge of the membrane properties of electrically excitable cells. The applicant is thoroughly experienced in working with experimental animals
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of the mean ocean circulation, resulting in large uncertainties associated with its role in future climate. Not understanding the control of the deep ocean circulation as it evolves through time is of obvious
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., phylogenetic models, ancestral state reconstruction, evolutionary rates analyses, etc.) is meriting. Bayesian phylogenetic approaches, applying machine learning to biological data, and tree building are not
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into structure-properties-performance relationship of the new materials. Main responsibilities Scientific research and education. The Postdoctoral Fellow is also expected to instruct students, asssist with project
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used to gain insight into structure-properties-performance relationship of the new materials. Main responsibilities Scientific research and education. The Postdoctoral Fellow is also expected to instruct
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related experiments. Experience using molecular techniques to assess parentage is also meriting. Finally, we are looking for a candidate who is willing to learn new methods as needed and can demonstrate a
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earth science applications is considered an advantage but not a necessity. The postdoc can learn necessary skills from the group. An interest for complex applications is however needed. She/he should have
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fundamental physics in the LIGO A+era. Responsibilities will include the development and application of advanced statistical inference and machine learning techniques for the analysis of compact object merger
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be given to research skills in one or more of: astronomical and cosmological data analysis, statistics, programming, radiative transfer and spectral analysis, numerical or machine-learning modeling