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. The overall aim of this project is to address these challenges by: Developing new data-driven and physics-based models of battery behaviour. Designing advanced BMS algorithms for real-time monitoring and
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safety. You will work on developing control algorithms all the way to performance assessment in test vehicles. The project combines theoretical aspects of control algorithms, experimental design, and
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, synthetic aperture radar, and optical spectroscopy, while bringing new capabilities in areas such as innovative sensor development, retrieval algorithms, novel applications, and other forward-looking areas
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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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well as the performance of active flight control systems remain underexplored. Addressing these gaps is essential for the success of this technology. This research project aims to support the development of safer, more
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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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, design and characterization of quantum processors Development and optimization of nano-fabrication processes for large-scale devices Development of optimal control techniches to achieve fast and high
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on building the next generation of quantum processors based on superconducting circuits. To achieve this ambitiuous goal, we have a variety of projects related to: Development and optimization of nano
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Join us in developing innovative flow control systems to enhance wind-assisted ship propulsion and support sustainable maritime technology! About us The Department of Mechanics and Maritime Sciences