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position for candidates interested in interpretable AI, stochastic optimal control, deep learning and high-impact research in sustainable mobility. About us The position is located at the Systems and Control
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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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of Electrical Engineering . You will be supervised by senior researchers with expertise in robotics, machine learning, automatic control, and optimization. The group leads and participates in numerous
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, localization, and sensing, with a focus on developing next-generation multiple-antenna systems while optimizing overall system performance. As a doctoral student, you devote most of your time to doctoral studies
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application no later than August 1, 2025. Project description Linear algebra expressions are evaluated in an efficient and robust way by mapping them to a carefully chosen sequence of calls to optimized
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the applicant: - For the dissertation and the subject relevant knowledge and skills, for example demonstrated strong background knowledge at advanced level especially related to automatic control, optimization
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successfully conducting research as well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control, optimization, and sensor
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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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application! At the Department of Electrical Engineering, Division of Automatic Control, we are now looking for a PhD student, to be admitted to the WASP graduate school. Your work assignments The research area