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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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to retrieve geophysical information from satellite data. Our research drives innovation in instrumentation and retrieval algorithms, and tackle climate change, air pollution, natural hazards, and land/ocean
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the theory of optimization algorithms and high-dimensional statistics to address some of the most fundamental questions in ML such as the behavior of neural networks. The environment of this project is highly
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strengthen your application: Experience with computational ship hydrodynamics Experience with STAR-CCM+ and Abaqus Knowledge of Control Algorithms (e.g. PID controller) Knowledge of Reduced Ordered Models What
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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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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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-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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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