10 computer-vision-and-machine-learning-"https:" PhD positions at UNIVERSITY OF VIENNA
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administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate: 01.02.2026 | Working hours
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chem- and bioinformatics to computer vision and social network analysis. Machine learning with graphs aims at exploiting the potential of the growing amount of structured data in all these areas
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art history, excellent English and or German, good computer skills (knowledge of a programming language would be advantageous). Extent of Employment: 30hours/week. Job grading in accordance with
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, machine learning, or (astro-)physics (in particular cosmology, galaxy formation, or general relativity) will be an advantage. What we offer: Inspiring working atmosphere: You will have the opportunity
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The University of Vienna is a cosmopolitan hub for more than 10,000 employees, of whom around 7,500 work in research and teaching. They want to do research and teach at a place that suits their
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gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning, quantum computing
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gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning, quantum computing
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bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 28.02.2029 Reference no.: 4985 Explore and teach at the University of Vienna, where more than 7,500 academics thrive on curiosity in
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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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no.: 5202 Among the many good reasons to want to research and teach at the University of Vienna, there is one in particular, which has convinced around 7,500 academic staff members so far. They see themselves