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investigate how the strengths of modern machine learning can be combined with the rigorous foundations of control systems theory to create a new generation of intelligent underwater robotic systems
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mechanisms that integrate queueing theory, traffic modelling, machine learning, and network-performance prediction for improving latency, reliability and fairness to support mission‑critical services
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of control systems theory to create a new generation of intelligent underwater robotic systems. The research will focus on developing hybrid learning–control architectures that integrate model-based control
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combining theory, research, and design practice Enthusiastic about connecting with people and engaging with real-world societal challenges Willing to contribute to a positive work atmosphere Emphasis will be
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. This may be extended to include potential flow theory based modelling as well. Develop deep learning surrogate models for fast prediction of motions, stresses, and loads Validate the deep learning model
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made in accordance with NTNUs guidelines for recruitment positions for general criteria for the position. Preferred selection criteria Solid understanding of control theory, including classical and
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
processing data streams, with theories and methods for planning and logistics. The PhD research will focus on AI-enhanced planning of operations in shipbuilding supply chains, examining how AI techniques can
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the position of PhD Candidate, code 1017, your gross salary will normally be NOK 580 000,-per annum depending on qualifications and seniority. A 2% statutory contribution to the State Pension Fund is deducted
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
of AI is both a technical and an integrative challenge. It requires combining what AI excels at, such as pattern recognition and processing data streams, with theories and methods for planning and
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for the position. Preferred selection criteria Solid understanding of control theory, including classical and modern control design methods Experience with modelling and simulation of dynamic systems Programming