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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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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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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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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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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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At the Faculty of Engineering and Science, Department of Materials and Production one or more Postdoc positions in the area of Optimization and Algorithm Design are open for appointment from April
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background in AI/ML technologies, including algorithm development, optimization, and data-driven modeling. Candidates are expected to demonstrate research leadership, with experience in national and
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stations, small satellite antennas, drone antennas, phased array antennas, massive MIMO antennas, metasurfaces, bio-electromagnetics, and wireless sensors. Qualification requirements Appointment as postdoc
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will: Develop and implement model-based and data-driven (AI) optimization algorithms for battery charging Integrate physics-informed models and data-driven tools to design health-aware charging protocols
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deliver a theoretical, algorithmic, and real-time implementation framework for on-the-fly autonomy in crowds. The resulting methods will (i) adapt to unpredictable human interactions that introduce high