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Applicants must hold a relevant master’s degree in telecommunications, computer networks, computer science, or a closely related field, with a strong background in computer networking and distributed systems
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a strong background in computer networking and distributed systems, including protocol stacks and network performance concepts. The degree must correspond to a five-year Norwegian degree programme
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publishing. Knowledge and experience of machine learning, preferably distributed machine learning. Knowledge and experience of coding and information theory. Knowledge and experience of communication networks
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on the problem of making distributed machine learning robust to network outages and computational bottlenecks. The work is part of the Norwegian national AI centre SURE-AI, and the PhD student will
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secure, resilient, and sustainable electricity distribution grid that ensures both the security of electricity supply and a path to a net-zero-emissions society. SecurEL aims to develop new knowledge
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, facilitating a secure, resilient, and sustainable electricity distribution grid that ensures both the security of electricity supply and a path to a net-zero-emissions society. SecurEL aims to develop new
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application process here. About the project The rapid integration of renewable energy sources (RES), distributed devices, and digital technologies is transforming traditional power systems into highly
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of renewable energy sources (RES), distributed devices, and digital technologies is transforming traditional power systems into highly interconnected Internet-of-Energy (IoE) ecosystems. In this context
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-ecological Observatory for Arctic Tundra (COAT) network, the candidate will develop and implement drone-based snow monitoring and associated validation workflows. Methodological challenges to be addressed
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Digital. The research focuses on advanced signal analysis and machine learning methods that enable robust operation and service continuity in future wireless networks under challenging radio conditions. As