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their passion and determination? We are currently seeking a/an University assistant predoctoral - PhD Position in Graph Learning 39 Faculty of Computer Science Startdate: 01.05.2026 | Working hours: 30
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for a/an University assistant predoctoral - PhD Position in Graph Learning 39 Faculty of Computer Science Startdate: 01.05.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1
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and determination? We are currently seeking a/an University assistant predoctoral - PhD Position in Graph Learning 39 Faculty of Computer Science Job vacancy starting: 01.05.2026 | Working hours
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2,900 work in 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
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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 Job vacancy starting: 01.02.2026 (MM-DD
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 3 months ago
-derived organoids, bioengineering and aging research. At AITHYRA, particular emphasis will be placed on the integration of multimodal biological data, development of deep learning and large language models
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). Internal communication is in German. Therefore a basic knowledge of German or the willingness to acquire such knowledge is essential. Work Location(s) Number of offers available1Company/InstituteConstruction
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knowledge of German or the willingness to acquire such knowledge is essential. Work Location(s) Number of offers available1Company/InstituteDepartment of Business Informatics, Production Management and
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Job description: 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
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skills, including proficiency in statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets