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generalizable, learning-based frameworks for dexterous robotic manipulation that are robust to environmental variability and transferable across diverse underwater robotic platforms. As a PhD in this position
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and complex terrains. To enable this vision, this position targets generalizable, learning-based frameworks for dexterous robotic manipulation that are robust to environmental variability and
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20th May 2026 Languages English English English The Department of Engineering Cybernetics has a vacancy for a PhD position. PhD Candidate in Reinforcement Learning and MPC Apply for this job See
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on the combination of Reinforcement Learning (RL) and Model Predictive Control (MPC). It will build up upon the work done at ITK on the topic. Several research focuses are considered: verification pathways in RLMPC
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Experience with AI / probabilistic AI / Machine Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Experience with predictive maintenance, fatigue, fault detection
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) for general criteria for the position. Preferred selection criteria Experience with AI / probabilistic AI / Machine Learning Experience with numerical optimization and MPC Strong programming skills (Python, C
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learned through publications in international scientific conferences and in at least three peer-reviewed journals. Participate actively in the EDISON project in direct connection with SINTEF and industry
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to machine learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You
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this webpage. The city of Trondheim is a modern European city with a rich cultural scene. Trondheim is the tech capital of Norway with a population of 200,000. The Norwegian welfare state, including healthcare
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. You will explore how emerging AI technologies—foundation models, generative design tools, agent platforms, reasoning engines, and reinforcement learning—can be adapted and extended for maritime design