134 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "The University of Gothenburg" positions at UNIVERSITY OF VIENNA
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(Master / PhD / Postdoc). Our expertise lies in quantum foundations, quantum information theory and quantum technologies. For additional information, please visit: https://dakic.univie.ac.at/ . Your future
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qualifications, preference will be given to female candidates. University of Vienna. Space for personalities. Since 1365. Data protection Application deadline: 01/23/2026 (Senior) Lecturer & Researcher https
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for the analysis of ONT data are expected. The successful applicant will work in the team of the Joint Microbiome Facility of the Medical Univeristy of Vienna and the Univeristy of Vienna (JMF), and support Centre
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optimization. Your future tasks Participation in research, teaching and administration in the field of Computer Science in Sport / Data Analytics in Sport. This involves: Developing and strengthening
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with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from
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the selection of strategic alternatives. Most of his research uses large-scale quantitative data analysis alongside semi-structured interviews. He uses unique data that he collects by scraping multiple data
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super-user, including data integration across donor, payment and expert systems To oversee end-to-end donor and sponsor processes (onboarding, mid- and back-office) and to manage the digital payment
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Contact information for at least 2 referees (can be added in the CV) Please apply via our job portal / Apply now button If you have any questions, please contact: Frank Melzner frank.melzner@univie.ac.at We
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spatio-temporal gradients. The study will be based on vegetation plot data in a transect encompassing four Central and Eastern European countries with different land-use histories and patterns of current
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-targeted Lab”. This position in the field of bioanalytical chemistry is dedicated to the development of innovative mass spectrometry-based workflows and data evaluation tools. The developed methods will be