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to enhance our multidisciplinary research at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement
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no particular order): Stability/Instability of coherent structures in 2D/3D flows. Stability/Instability in geophysical fluids and plasma physics. Spectral theory for non-self-adjoint operators. Candidates must
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. The candidate is expected to carry out cutting-edge research on the physical layer design and optimization of RHS-enabled or RIS-assisted MIMO communication systems using tools from information theory
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technologies and data sources; as well as the combination of traditional traffic flow theory concepts with new empirically derived models and data science ideas. Applicants must have received a PhD in
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to improve our research, and (ii) exemplary passion and motivation to enhance our multidisciplinary research at the intersection of control theory and machine intelligence. Methodologies of interest include
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-assisted MIMO communication systems using tools from information theory, statistical signal processing, optimization theory, machine learning and artificial intelligence. The candidate is expected
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-edge machine learning, including Large Language Models (LLMs), to enhance decision-making and planning in robotic systems. Qualifications: Applicants must have a PhD in Robotics, Control Theory
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trajectories and enhance interaction safety and robustness. Learning for Human-Robot Interaction: Integrate machine learning techniques, such as reinforcement learning and generative models, with control theory
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. Candidates must hold (or be close to completing) a Ph.D. in developmental or social psychology, or a related field. PhD holders with a strong publication record, a background in attachment theory, and
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. The project activities will involve the development of the theory and implementation of the advanced mechanics and numerical models as well as constitutive model calibration and validation based on physical