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Field
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Research Framework Programme? Other EU programme Reference Number 11/2026 Is the Job related to staff position within a Research Infrastructure? No Offer Description Applications invited for the following
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three campuses. A four-year MD program and the MD/PhD program are located on the Twin Cities campus in addition to MD programs at regional campuses in Duluth and St. Cloud. Apply for Job
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of advanced statistical methodologies, and supporting research on high performance and cloud computing. The successful candidate will also be expected to offer 2-3 advanced technical or methodological workshops
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2027 - 03:37 (UTC) Country Luxembourg Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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University of Applied Sciences in Nowy Sacz (Akademia Nauk Stosowanych w Nowym Sączu) | Poland | 3 months ago
teaching in the field of computer networks, system and network security, cybersecurity of IT systems, and security of cloud systems and services, 4) the ability to work effectively in a team, 5) willingness
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Research Framework Programme? Horizon 2020 Is the Job related to staff position within a Research Infrastructure? No Offer Description Ref No.: ERA-PD-ECD-198 UP TO TWO POST-DOCTORAL RESEARCH FELLOW
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum