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-driven approach for optimizing the growth of semiconductor materials by combining machine learning with a physics-based understanding of the growth process. Doping and processing of ultra-wide bandgap
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September 2027, with some flexibility upon agreement. - A PhD in Mathematics (completed by the start date) is required. Profile We welcome applications from highly motivated candidates with a background in
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discoveries made in the laboratory with meaningful improvements in patient care. You will be part of a research environment that values curiosity, creativity, scientific rigor, and continuous learning while
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microscopy. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master
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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and
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technologies. The candidate will work at the intersection of Artificial Intelligence, Scientific Machine Learning, and Energy Materials, contributing to the design of self-driving laboratories and no-code
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will join a research group working on longitudinal models of multiple chronic diseases across the life course. The postdoctoral researcher will contribute to building and evaluating machine learning
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. The successful applicant will possess a PhD or equivalent doctoral degree in the social sciences, including political science, public policy, political ecology, law, geography or another relevant field with some
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(postdoc) Limited until: 31.10.2032 Reference no.: 6212 There are many good reasons to want to research and teach at the University of Vienna. And one is why around 7,700 academic staff members before you
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mechanics, machine learning or applied/computational mathematics. ● Demonstrated ability to carry out original mathematical derivations and/or develop computational tools, evidenced by publications