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- NTNU Norwegian University of Science and Technology
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across the SURE-AI network, and participate in all relevant centre activities. Required selection criteria You must have a relevant Master's degree in electronics, mathematics, data science, communication
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Master's degree in Cybernetics or equivalent, with a strong training in engineering mathematics and optimization. Your course of study must correspond to a five-year Norwegian course, where 120 credits have
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thesis students related to the project Required selection criteria You must have a relevant Master's degree in Cybernetics or equivalent, with a strong training in engineering mathematics and optimization
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be met: Advanced methodological knowledge in the form of completed master's course or documented exam in methods / theory of science at master's level A Scientific article assumed in a peer-reviewed
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& Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame
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obtained through studies in engineering cybernetics, robotics, control engineering, automation, electrical engineering, marine engineering, mechanical engineering, applied mathematics, or similar engineering
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juveniles. Both global, regional and local perspectives and applications, across finfish and shellfish, are encouraged, with the aim to develop general theory around optimising marine spatial management
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will join and become part of an international research environment in quantum condensed matter theory and experiments The PhD candidatewill explore quantum many-body phenomena in emerging two-dimensional
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, or simulation-based evaluation Background in optimization, resource allocation, scheduling, or graph-based network analysis Exposure to resilience, reliability engineering, or security analysis (including
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., entanglement, fidelity/noise, quantum repeaters) Experience with network modelling, performance analysis, or simulation-based evaluation Background in optimization, resource allocation, scheduling, or graph