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operation Quantum algorithm implementation and benchmarking About you You have a relevant Masters deegree corresponding to at least 240 higher education credits (Physics, Nanotechnology, Engineering, Computer
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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
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a theoretical framework and novel algorithms. This project will be conducted in close collaboration with Axel Flinth from Umeå University (who will act as WASP co-supervisor) and Pan Kessel from
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the Tergite software stack, enabling the execution of complex quantum algorithms on our 25-qubit processor. A key focus is automating calibration to ensure reliable single- and two-qubit gate operations, as
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, water, wave) and transportation systems (road, rail, marine, aerospace), but other areas are also welcome. The research orientation may cover a wide range of areas from fundamental modeling and algorithm
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driving range. The project aims to analyze algorithms for predicting the remaining driving range of EVs and suggest ways to improve the current state of the art. The idea is to develop a model-based
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(e.g., conduct systematic literature reviews; design and conduct empirical studies; develop algorithms and theories, implement prototype programs to test their ideas, interact with industrial companies
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of nano-fabrication processes for large-scale devices Design and implementantion of automatic calibration techniques for fast tune-up Implementation and benchmarking of quantum algorithms Qualifications We
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for describing and reasoning about algorithms for translating from quantum circuits to pulse schedules, and optimizations that can be performed on the pulse schedule level, while at the same time following known
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of quantum processors Design and simulation of quantum processors Optimal-control techniques for high-fidelity qubit operation Quantum algorithm implementation and benchmarking About you You have a relevant