900 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" uni jobs at Nature Careers
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Job description: The University of Vienna is a cosmopolitan hub for almost 11,000 employees, of whom around 7,700 work in research and teaching. They want to do research and teach at a place that
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| Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 13.11.2026 Reference no.: 5104 Explore and teach at the University of Vienna, where more than 7,500 academics thrive
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, running expenses, and competitive start-up packages. Additionally, the successful applicant is expected to acquire external research funding. Zurich's scientific environment encompasses a rich spectrum of
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) Limited until: 28.02.2030 Reference no.: 5251 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
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agreement: §48 VwGr. B1 Grundstufe (praedoc) Reference no.: 5266 Among the many good reasons to want to research and teach at the University of Vienna, there is one in particular, which has convinced around
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immunology; Teach students at all academic levels in the field of biological sciences; Supervise undergraduate and graduate students in the field of biological sciences; Contribute to the effective functioning
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productivity Prior research experience in primary or induced neurons is a plus. Successful applicants are expected to demonstrate a high level of independence, a willingness to learn new approaches and
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well as facilitate learning and knowledge sharing across the agency. The successful candidate will have: Advanced technological knowledge and experience in Nuclear Magnetic Resonance (NMR) spectroscopy, including
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exercises, and assessments aligned with current industry standards. Creating an engaging, application-oriented learning environment. Mentoring and advising students in course projects and applied data
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applications, including search engines, chatbots, and text classification systems. It will also present and refresh the relevant deep learning architectures on which recent LLM architectures capitalise