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/research-groups/sigcom/ . The successful candidate is expected to assume a leading role in advancing research and experimental work focused on software-defined networking (SDN), network function
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The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre...
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systems, carrying out both upstream activities such as requirements quality assurance and regulatory compliance, as well as downstream verification & validation activities, such as software testing and
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-of-the-art facilities include two physical laboratories, the LunaLab and Zero-G Lab, and a new Robotic Manipulation Lab – dedicated environments for researchers to test their results on advanced equipment
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to projects at the intersection of software-defined networking (SDN), software engineering, and quantum communications. A key focus will be the development of KMS (Key Management System) software
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compliance, as well as downstream verification & validation activities, such as software testing and runtime verification. For further information, you may refer to https://www.uni.lu/snt-en/research-groups
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grant applications Good self-organisational and communication skills for a small team Flexibility to quickly adapt to new tasks (multitasking) Ability to travel to cooperation partners within the greater
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limited to: Designing, implementing, and maintaining scalable pipelines that ingest raw on-chain data (blocks, transactions, smart-contract events) from public blockchains into research-grade databases
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undertaken will form the basis for defining the next stages of the work to be carried out. Responsabilities: Conduct research on the history of migration in Luxembourg and the Greater Region, based on a
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required to perform the following tasks/will do research on the following topics: Software engineering practices for machine learning Tabular machine learning Large language models on structured and semi