89 learning-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions in Switzerland
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the research profile of the Department of Ancient Civilisations. They are expected to have teaching experience and to be keen to teach undergraduate and doctoral students in innovative and practice-oriented
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of computational and applied mathematics, including but not limited to data-driven numerical modeling, scientific machine learning and AI for science and engineering, computational uncertainty quantification
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combines flexible, location'independent part'time distance learning with an active, interdisciplinary research culture on its campus in Brig. Across five faculties ' Law, Psychology, Business & Economics
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sustainabilityIn line with our values , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity
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the field of the Mathematical Foundations of AI for Science and Engineering (also known as Scientific Machine Learning). The new professor will lead an internationally high-profile research and teaching
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, including the mathematics of deep learning, large language models, and computational methods for AI systems. Successful candidates are expected to provide academic leadership by establishing, managing, and
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numerous opportunities to exchange ideas with other doctoral students, receive feedback on the work in progress, and acquire the skills necessary for an academic career. The Doctoral Program also supports
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combines flexible, location'independent part'time distance learning with an active, interdisciplinary research culture on its campus in Brig. Across five faculties ' Law, Psychology, Business & Economics
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learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment
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cohort of doctoral researchers and benefit from ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation