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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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for a PhD position that combines research in the field of intelligent mission planning and learning-based optimization with real-world applications, in collaboration with Volvo Group. This is an ideal
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technology, building physics, HVAC systems, computer science and control systems architecture, thereby advancing all disciplines involved. The project is a collaboration with Building Services Engineering
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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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Technology (Maria Taljegård). Main responsibilities Plan and perform research under the guidance of supervisors Apply and develop energy- and environmental systems assessment methods and tools Collect and
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-fabrication processes for superconducting devices Automatic bring-up and calibration of quantum processors Design and simulation of quantum processors Optimal-control techniques for high-fidelity qubit
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, embedded systems, remote sensing data or modeling of satellite orbits. Communication skills in Swedish are valuable, but not required. What you will do Major responsibilities include: design and analysis
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% of teaching and departmental tasks are included. Main tasks as a PhD student are to: Design and conduct empirical research in collaborative projects with companies Publish research results in high-impact
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correctly. Project description The goal of this PhD project is to develop techniques for the design and verification of assured ACPS with a focus on runtime assurance. You will develop theory and tools