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spatial monitoring, landslide analysis and modeling Profound field experience Greatest interest in landslide early warning systems inclusive installation and maintenance of field devices, data transfer
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focus on the individual effects of artificial intelligence. You have in-depth knowledge of methods of empirical social research, especially quantitative methods and statistical data analysis. You have
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. Proficiency in data analysis and interpretation, as well as the ability to critically analyse scientific literature. Excellent written and oral communication skills in English (IELTS score = 6.5). Strong
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expertise in the area of bioorganic chemistry, peptide synthesis and protein engineering as well as experience with peptide and protein purification, analysis and preferably also with biophysical
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, the organization of training interventions, as well as the collection, documentation, analysis, and publication of physiological and mental performance indicators. In addition, active participation in teaching and
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the field of educational science. At the department, a 7-member administrative team organises the academic staff, consisting of 70 members, who are dedicated to the description, critical analysis and
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, Design and Architecture of Microservice Design, Software Engineering for Machine Learning, Code Analysis/Generation for Machine Learning and MLOps, and Continuous Delivery and/or MLOps. You participate in
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/culture/media studies profound knowledge of Spanish or French as well as English familiarity with the methods of cultural studies and/or literary discourse analysis ability to think analytically
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to be coupled to model-free analysis of molecular dynamics simulations. This includes work in the biochemical wet-lab as well as with prototype NMR spectroscopy and computational tasks. You have previous
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contribute to the setup and operation of upcoming ACTRIS National Facility for aerosol in-situ at the University of Vienna You will develop novel methods and analysis tools for in-situ aerosol data using state