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and physics-based models for battery behaviour. Designing advanced BMS algorithms for real-time monitoring and control. Creating generalizable tools applicable to various battery types, geometries, and
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the research project and the division of Astronomy and Plasma Physics The successful candidate will work on developing and testing algorithms for 3D magnetic field reconstruction. The position will
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modular, scalable, and transparent control algorithms suitable for real-time implementation across different vehicle platforms. - Contribute to theoretical developments in stochastic model predictive
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system complexity. Your work will include: Developing modular, efficient, and transparent control algorithms. Combining model predictive control with learning-based motion prediction under uncertainty
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of numerically stable and efficient computational algorithms, their implementation and testing with computer programs, both in simulation as well as real robotic systems. Information about the project and the
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-fidelity qubits operations Design and implementantion of automatic calibration techniques for fast tune-up Implementation and benchmarking of quantum algorithms About you You have a relevant PhD deegree
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operation Quantum algorithm implementation and benchmarking About you You have a relevant Masters deegree corresponding to at least 240 higher education credits (Physics, Nanotechnology, Engineering, Computer
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driving range. The project aims to analyze algorithms for predicting the remaining driving range of EVs and suggest ways to improve the current state of the art. The idea is to develop a model-based
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passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
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the Tergite software stack, enabling the execution of complex quantum algorithms on our 25-qubit processor. A key focus is automating calibration to ensure reliable single- and two-qubit gate operations, as