111 web-programmer-developer-"https:"-"https:"-"https:"-"https:" positions at University of Vienna
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members of staff. Research and teaching cover literary and cultural studies and linguistics. Currently 2.500 students are enrolled in its Bachelor, Masters, Diploma, PhD and Teacher Education programmes
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on the Doctoral Programme in Pharmacy at the University of Vienna. Recipients will be employed as university assistants predoctoral at the Department of Pharmaceutical Sciences. At the time of application, work
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for optimization under uncertainty, in particular decision-focused learning. • You are an experienced programmer, preferably using the Python programming language. • You are highly organized and have excellent
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importance on the personal development of students. We support students in developing a curious and critical mindset, thus laying the foundation for the professional challenges of the next generation. As a
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addition to providing a solid academic education, we also place a special emphasis on the personal development of students. We support students in developing curiosity and critical thinking, thereby creating a solid
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on a topic relevant to the department, such as fertility and family, mortality and health, migration, or population analysis and human capital. The candidate should present a clear personal work plan for
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Data Stewardship program. The Faculty of Earth Sciences, Geography and Astronomy (Fakultät für Geowissenschaften, Geographie und Astronomie / FGGA) is therefore looking for a highly motivated person (m/f
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Postdoc in Invertebrate Evolution with a focus on either comparative morphology, EvoDevo, or phylogenomics. We are a dynamic, international group situated in the recently built University of Vienna Biology
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experience. Equal opportunities for everyone: We look forward to diverse personalities in the team! How to apply: Application documents should include: letter of motivation including time plan for doctoral
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to the department Ph.D. program and will work on the development and analysis of statistical methods for machine learning, particularly in the context of high-dimensional models and with a particular focus on methods