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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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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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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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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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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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characterization, and digital twins to predict and compensate for fluctuations in viscosity, moisture content, and thermal behavior. Your competencies The successful candidate will design algorithms that enable
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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
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algorithmic solution development. The group focuses particularly on automated decision-making in autonomous cyber-physical systems, combining mathematical optimization, machine learning, and decision theory
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and design challenges that address the following (but are not limited to) challenges for quantum computing and communicating systems: - Design and semantics of distributed quantum programming and
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are not limited to) challenges for quantum computing and communicating systems: - Design and semantics of distributed quantum programming and modelling languages - Sound and complete deduction methods