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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
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, or mixed methods; research ethics). Demonstrated knowledge in the field of the project (HCI for designing for learning; educational technology with a focus on interaction design; technology focus: AI-based
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Veterinärmedizinische Universität Wien (University of Veterinary Medicine Vienna) | Austria | about 2 months ago
programming skills in general • Basic knowledge of German (level A2) • Excellent command of English, both written and spoken (level C1) LanguagesENGLISHLevelExcellent LanguagesGERMANLevelBasic Research
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of qualification theses Participation in the self-administration of the university, the department, the institute and the environmental and agricultural sociology working group Knowledge transfer and engagement in
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Biology Building (UBB). The AC is grounded on basic research on animal behavior and cognition, but aims to connect researchers from related fields of expertise within the Faculty of Life Sciences, such as
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of Vienna Biology Building (UBB). The AC is grounded on basic research on animal behavior and cognition, but aims to connect researchers from related fields of expertise within the Faculty of Life Sciences
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Veterinärmedizinische Universität Wien (University of Veterinary Medicine Vienna) | Austria | 3 months ago
of position 3) Very good command of English (C1), Good knowledge of German language (B2) 4) Excellent communication skills and interdisciplinary team-work orientation. Additional Information Additional
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Building (UBB). The AC is grounded on basic research on animal behavior and cognition, but aims to connect researchers from related fields of expertise within the Faculty of Life Sciences, such as
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the University of Vienna Biology Building (UBB). The AC is grounded on basic research on animal behavior and cognition, but aims to connect researchers from related fields of expertise within the Faculty of Life
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with biological knowledge using AI/ML and other computational approaches in cancer and immunology research. We focus on approaches that produce robust, reliable, and ideally interpretable results