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sensor integration. Experience with SLAM algorithms (vision-, acoustic-, or inertial-based), state estimation (e.g. Kalman filtering, pose graph optimization), or collaborative positioning is highly valued
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and communication systems. You will work with real measurement data and participate in both algorithm development and experimental validation. You will collaborate with industrial and academic partners
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continuum robotic hardware systems Integration of miniaturized actuators and sensors into continuum robotic platforms Calibration and experimental validation of the developed robotic systems, including
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interventions or neurosurgery. Responsibilities include: Design and construction of miniaturized continuum robotic hardware systems Integration of miniaturized actuators and sensors into continuum robotic
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communication infrastructure design and evaluation, reliable communication technologies, protocols and algorithms, internet of things and wired/wireless communication at all network layers. The candidate should
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control strategies based on Model Predictive Control (MPC) and real-time sensor feedback to dynamically adjust process parameters and recipes. The research will integrate data-driven models, material
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is to develop machine-learning-based algorithms for transmitter pre-distortion and receiver post-distortion architectures that enable distortion-free quantum communication systems. A key focus will be
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algorithms for physiological data Development of mobile applications for sensor integration and patient use Support in setting up cloud-based infrastructures for secure data collection and storage
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theory, and is expected to have experience with the practical implementation of control algorithms. Who we are Learning and Decision at AAU, founded in 2020, focuses on developing mathematical methods
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Relate parallelism to applications, e.g., algorithmic parallelism, multi-tasking, etc. Address nonlinear equalization in optical signal transmission and provide a comparison with neuromorphic electronics