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This PhD position presents a valuable opportunity to explore shared control between humans and vehicles utilizing steer-by-wire systems and to investigate how this approach can enhance vehicle
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What if you could design systems that not only follow instructions — but understand intent and guarantee correct behavior over time? We are looking for up to two PhD students who want to explore
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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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This PhD position offers a unique opportunity to advance safe and transparent control for autonomous, over-actuated electric vehicles. You will work at the intersection of model predictive control
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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 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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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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We invite applications for several PhD positions in experimental quantum computing with superconducting circuits. You will work in the stimulating research environment of the Wallenberg Centre
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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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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