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to study grid stability, fault propagation, and recovery dynamics. Analyzing control and protection strategies using high-resolution time-domain models. Developing dynamic models for grid-forming and grid
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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Engineering. Food approved microorganisms will be optimized using classical mutagenesis/screening and adaptive evolution. Genetic engineering will be used for guidance and to elucidate the effect of introduced
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. The candidate will study architectural models, including the placement and roles of quantum repeaters, memories, and control-plane functions, and how these integrate with classical networking and orchestration
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devices Optimization of waveguides for photon pair sources Design and characterization of sources of entangled quantum states. SiCPIC PhD student Qualifications Candidates must have a two-year master's
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, statistics, machine learning, control, computer science, or a related area that is considered relevant for the research topic of the project, or have completed courses with a minimum of 240 credits, at least
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will develop novel ligands for a validated drug target in acute myeloid leukemia. Building on our promising preliminary results, you will optimize and expand the hit discovery using computer-aided tools
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architectures, while also being in line with the Open RAN initiative; Novel AI-based resource allocation solutions, targeting the optimal utilization of both networking and computational resources. In
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control-plane functions, and how these integrate with classical networking and orchestration. The work will also explore operational control in quantum networks, including quantum-aware resource allocation
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(HCI). The position will collaborate closely with (1) the NTNU Shore Control Lab (SCL), a design and human factors research environment focusing on human-centered design of technology for safety