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elsewhere in the job posting. Work Experience: No additional work experience unless stated elsewhere in the job posting. Skills: Adobe Creative Cloud, Adobe Illustrator, Adobe InDesign, Computer Literacy
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development for embedded systems and explore cloud storage solutions for video recordings. Ensure compatibility of the device with standard sterilization processes. Support the compliance with regulatory
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description: At the University of Vienna, almost 11,000 people work
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of collaboration with top researchers within cybersecurity, networking, AI planning, and cloud computing, both within our department and two other universities in Sweden (KTH and Umeå University). The project is
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major data science application domains (e.g., healthcare, medical devices, social networks) or societal infrastructures (e.g., transportation, smart grids, smart cities, cloud computing). All candidates
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transcriptomics. Experience with Neuro Degeneration biomarker datasets (PET, MRI, immunoassays, ADNI). Prior experience working in collaborative, fast-paced research environments. Familiarity with cloud computing
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. Experience using Emburse. Experience managing multiple calendars. Event coordination and planning experience. Knowledge of SharePoint or Google Docs or other cloud-based storage. Ability to deal effectively
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the Enrollment Office to streamline integration of enrollment technologies with enterprise systems (LMS, SIS, ERP, Office 365) Manage integrations between enterprise systems and cloud-based platforms using REST
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Creative Cloud, Digital Audio Workstations, non-linear editing systems, live production tools). Manage software for equipment check out (Connect2) and ensure equipment storage is accessible and well
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with experience in ligand discovery. Our research group is focused on developing state-of-the-art computational methods for ligand/drug discovery, using machine learning, high-performance/cloud computing