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. • Develop and evaluate artificial intelligence and reinforcement learning approaches for image-guided vascular navigation. • Design experiments, validation strategies, and quantitative performance
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. • Develop and evaluate artificial intelligence and reinforcement learning approaches for image-guided vascular navigation. • Design experiments, validation strategies, and quantitative performance
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opportunity to work at the forefront of brain-inspired control and soft robotics. At DTU, you will develop novel control architectures that combine model-based methods, reinforcement learning, and
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— reinforcement learning and neural- network-based optimization — have transformed quan- tum control on superconducting, trapped-ion and spin platforms [7, 8], but remain essentially unexplored for nonlinear
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trustworthy reinforcement learning methods for real-world systems, with particular emphasis on planning and tree-search algorithms, including Monte Carlo Tree Search approaches. The activities will involve the
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. A strong background in energy-system modelling, mathematical optimization, machine learning, reinforcement learning, or data-driven decision-making is appreciated. Experience with programming and
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fluency in English Experience relevant to at least one of the projects, including world models, video generation, generative modeling, self-supervised learning, meta-learning, reinforcement learning
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-framework such as MUJOCO Reinforcement learning for trajectory planning Job Requirements: PhD in Mechanical engineering, Electrical engineering, or related fields Good publication track record Creative
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-framework such as MUJOCO Reinforcement learning for trajectory planning Job Requirements: PhD in Mechanical engineering, Electrical engineering, or related fields Good publication track record Creative
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demographics remain lacking. This project proposes to bridge this gap by transferring state-of-the-art methods from humanoid robotics – reinforcement learning, whole-body model predictive control (MPC