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| Classification CBA: §48 VwGr. B1 Grundstufe (praedoc) Limited contract until: 30.09.2030 Job ID: 5833 Among the many good reasons to want to research and teach at the University of Vienna, there is one in
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good knowledge of English (C1 or higher) Experience with 3D printing and CAD programs Advanced computer/IT skills (troubleshooting, Windows administration etc.) Reliable, regular presence in
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Iventa Austria Personalwerbung GmbH | Graz 12 Bez Andritz, Steiermark | Austria | about 2 months ago
Lab for foundational and application-driven research in machine learning, data science and digital transformation. The professorship maintains close ties to computational social science, data science
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systems, and machine learning methods for industrial high-performance visual quality inspection. The objective of this master’s thesis project is to evaluate the potential ofmaterial aware3D
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practices • Coding and/or machine learning We further expect you to have • Publications in high-quality outlets (peer-reviewed according to subject-specific standards) • Presentations
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the start of employment) Teaching skills/experience in e-learning Comprehensive computer skills, particularly MS Office Required are a profound knowledge of Catholic systematic theology and philosophy
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. b (postdoc) Limited contract until: 14.10.2032 Job ID: 5906 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us
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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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Grundstufe (praedoc) Limited contract until: Job ID: 5717 Explore and teach at the University of Vienna, where more than 7,500 academics thrive on curiosity in continuous exploration and help us better
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measure 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