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properties through computer simulations. The project also includes validation of materials in the lab and translating the findings into practical recommendations for use in real power-electronics environments
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At the Faculty of Engineering and Science, Department of Mathematical Sciences, one or more PhD stipends/Integrated PhD stipends in inference and modeling of Quantum transduction processes
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engineering, or a closely related discipline. A strong background and demonstrable interest in structural mechanics as well as engineering modelling, analysis and simulation is essential. Further, the ideal
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design and laboratory experimentation, you will first explore a broad range of sodium-oxide glass compositions using advanced computer models to predict how well ions can move through them. Based
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dynamic, crowded environments. As a PhD candidate, you will develop methods that combine data-driven autonomy with formal safety guarantees and validate them in real time through simulation and experimental
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engineering, or similar. Solid mathematical and analytical skills, including signal processing and optimization. Knowledge about classical and/or quantum data communication, including for instance error
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development and creative design processes. The lab will provide access to shared resources, joint supervision, and interdisciplinary training in AI, Life Cycle Assessment (LCA), and computational design tools
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C++). Familiarity with robotics software frameworks such as ROS, MATLAB/Simulink, and simulation environments like Gazebo or Unity. Experience developing software for real-time sensor processing
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for establishing properties of quantum programs - Reduction methods and metrics for quantum systems - Decision diagrams for efficient analysis and simulation of distributed quantum programs - Statistical model
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around building and deploying AI in healthcare. The main case will be automation of anesthetic pre-screenings. Potential auxiliary cases include evaluation of laparoscopic surgery and AI assistance