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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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Vacancies PhD position on indirect combustion noise; Fluid Mechanics; CFD; Aeroacoustics Key takeaways Simulation-based development of indirect combustion-noise theory To remedy ineluctable
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traffic flow theory with machine learning, and with that, the best of both worlds: theory and logic where necessary, data-driven where possible. This innovative new approach enables more efficient and
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techniques, including KKT conditions or ADMM algorithms, would be appreciated • Interest in flexibility management, game theory, equilibrium problems, or multi-energy systems • Strong analytical, research, and
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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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traffic flow theory and simulation. You are a machine learning enthusiast (and realist). You love coding and have proven experience in e.g. Python, Matlab, JAVA, C#. You can present and communicate your
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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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Engineers usually want predictability. This project embraces chaos! – Excited about control theory? Then join us to build the math of chaotic sampling for greener and more secure control systems