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approaches in Human-Computer Interaction and Contextual User Experience. The Center for Technology Experience is acting in a variety of challenging application contexts. We are passionate about addressing the
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sciences and artificial intelligence, and translate your findings to improve human health? Are you excited to develop and use machine learning approaches to gain new understanding of the molecular physiology
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their application. We are looking for graduates from: Renowned management programs: e.g. M.Sc. in Business or Adjacent disciplines such as economics, psychology, engineering or computer sciences: e.g. M.Sc. in
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. Close collaboration with research groups in theoretical solid-state physics/chemistry, machine learning, and artificial intelligence is expected. Close cooperation with industrial partners and the
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. M.Sc. in Business or Adjacent disciplines such as economics, psychology, engineering or computer sciences: e.g. M.Sc. in economics, M.Sc. in industrial engineering, M.Sc. in information management, M.Sc
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Analysis and Machine Learning. The research areas cover a wide range of challenging topics such as (infinte dimensional) stochastic analysis, affine and polynomial processes, rough paths, signature methods
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. 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 such as
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spoken German and English skills Very good computer skills Interdisciplinary knowledge in the field of the dissertation Please enclose written reflections on your dissertation project with your application
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: 30.09.2028 Job ID: 4640 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 if you’re passionate about groundbreaking
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lit. b (postdoc) Limited until: 31.12.2029 Reference no.: 4269 Among the many good reasons to want to research and teach at the University of Vienna, there is one in particular, which has convinced