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: 3967 We invite applications for a PhD position in Plant Ecological Genomics at the University of Vienna, Austria. The position is part of an international research project co-funded by the Austrian
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knowledge of research practice, as well as a broad understanding of how research operates and how data and software underpin reproducible research Experience with designing and delivering training Very good
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Completion Contract for fellows of the DocSchool who have held a position as University Assistant (praedoc) or a PhD position at the University of Vienna before. This Completion Contract aims to support a
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particular terrestrial/mobile laser scanners and UAV laser scanners Completion of a PhD is desired Publications in SCI journals Teaching courses in German and English in the field of forest inventory, forest
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submission of their thesis. All VDSEE PhD-candidates are eligible to apply, regardless of whether they have previously had a contract with the University of Vienna. The funding period is 4 months during which
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to focus on the completion and submission of their thesis. All VDSEE PhD-candidates are eligible to apply, regardless of whether they have previously had a contract with the University of Vienna. The funding
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): Your qualifications as an Ingenious Partner : PhD in biology, microbiology, or other related field experience in plant microbiology / microbiome research Experience in acquisition of third-party funding
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function. You will be joining a vibrant research group focused on the interstellar medium, star/planet formation and Galactic climate, with several PhD students and postdoctoral researchers. We expect you to
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competencies: Jurisprudential methods Knowledge of principles of teaching High ability to express yourself both orally and in writing Excellent command of written and spoken German High command of written and
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administration as well as in teaching and research administration The research should focus on clustering methods, especially deep clustering and representation learning for complex and high-dimensional data