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- Cluster of Excellence Circular Bioengineering
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- the the Erich Schmid Institute of Materials Science of the Austrian Academy of Sciences (ÖAW)
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cellular communities ultimately aiming at the ambitious goal of single cell analysis. Your responsibilities: Develop mass spectrometric methods advancing circular bioengineering Develop mass spectrometric
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, documented by peer-reviewed publications Basic knowledge of molecular and immunohistochemical methods, as well as bioinformatics and statistics International research experience (e.g. postdoctoral training
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cellular communities ultimately aiming at the ambitious goal of single cell analysis. Your responsibilities: Develop mass spectrometric methods advancing circular bioengineering Develop mass spectrometric
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Materialist theories Developing appropriate (design) research methods Project management About the project:Thinking with Things is funded by the Austrian Science Fund (FWF) and will start on January 1, 2026. It
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the the Erich Schmid Institute of Materials Science of the Austrian Academy of Sciences (ÖAW) | Austria | 30 days ago
-depth understanding of methods and, if necessary, expand this with regard to the specific research needs of the institute. Your Tasks The successful candidate will be part of an international team whose
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at an international level, interest in translating linguistic and pedagogical research into methods and materials for English teaching in schools, international experience, very good command of German (C1
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or related disciplines. Outstanding dissertation. Proficiency in social science qualitative research methods. A keen interest in acquiring advanced skills in discourse analysis and social network analysis
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mixed methods. Contribute to the HCI and EdTech research team (https://www.tugraz.at/en/institute/hcc/research/research-groups/team-pammerschindler ). Engage in interdisciplinary collaboration with
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educational philosophy focus Knowledge of structures and developments in adult and continuing education Willingness to acquire comprehensive skills in qualitative and/or quantitative methods of empirical
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, with emphasis on PHY and MAC layer methodologies. Develop scalable hierarchical learning methods for wireless systems, integrating selfsupervised adaptation with cross-level knowledge transfer. Publish