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to apply Website https://iventajobdata.eu/bestmedia/img/2502621/2003273/cl/147a658ff5227583c5e60… Requirements Specific Requirements Your personal sphere of influence: The Department of Behavioral and
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Operations Research (https://isor.univie.ac.at/ ) of the University of Vienna is offering a PhD position in the area of Mathematical Statistics and Machine Learning, starting on 01 March 2027. The group's
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at the University of Vienna thrive on continuous exploration and curiosity and help us better understand our world. Does this sound like you? Then join our accomplished team! Where to apply Website https
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our areas of academic research here: https://lit-ktf.univie.ac.at Your future tasks: You will actively participate in research, teaching, and administration, which means: Preparing a dissertation in our
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, the outstanding research projects, the interdisciplinary cooperation and the collegial exchange in our faculty. Our current research focuses on Software Protection, Machine Learning/AI und Security und Security and
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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of the ./studio3 research group, addressing current transdisciplinary issues at the interface of architecture, art, culture, new media and innovative technologies, in-depth knowledge of analogue and digital artistic
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phenotyping, machine learning and advanced statistical approaches. The doctoral project will be developed jointly with the successful candidate and tailored to their methodological background, previous research
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contributions to improve the quality of obstetric care. Ideally, modern and future-oriented aspects will be considered, including digitization, smart sensors, Artificial Intelligence (AI), and machine learning in
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data-driven modeling. The successful candidate will develop and apply data-driven methods for chemical discovery and molecular design. These methods include machine-learned interatomic potentials, data