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; experience abroad an advantage Good computer skills Please enclose written reflections on your dissertation project with your application (max. 5 pages) Ability to work in a team, motivation, commitment
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Prompting Strategies and Eco-Feedback for Efficient Use of Large Language Models A summary of the planned project is available at https://computational-sustainability.at/HAI4S/ . The dissertation will be co
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the opportunity to also contribute to our well-established collaboration with the Faculty of Electrical Engineering and Information Technology. Applications such as Machine Learning are driving up computational
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Willingness to participate in ongoing research projects of the Division of Prehistory and Early History Practical experience in geoarchaeological laboratory work Excellent computer skills (GIS, databases
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the Tenure Track position, such as Machine Learning, Neural Networks, Optimization, Applied Harmonic Analysis, Inverse Problems, Quantum Information, and other relevant areas. Additionally, the position will
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environment with more than 70 researchers active in the fields of computer graphics, computer vision, image processing, visualization, virtual, mixed and augmented reality, and machine learning for visual
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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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interactions help facilitate active scientific exchange and foster a good atmosphere, and therefore play a big role in our team. Learn more about us here: https://swa.cs.univie.ac.at/ Your future tasks: You
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Language Processing and Machine Learning, in close coordination with the AI & Cloud Manager Collaboration on organizational and administrative tasks as well as evaluation measures, particularly those derived from
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the final phase of the doctoral studies. The successful applicant will get a contract as university assistant (prae doc) and will be working in the research group “Data Mining and Machine Learning