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. A strong background in energy-system modelling, mathematical optimization, machine learning, reinforcement learning, or data-driven decision-making is appreciated. Experience with programming and
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). Expertise in the following areas is considered beneficial: scripting (Python, bash, etc.), high-performance programming (Fortran/C/C++, CUDA, etc.), ML libraries (sklearn, Pytorch, etc.). In your CV or
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design, clean-room techniques, simulations, data analysis, and instrument programming. Our group is doing research on circuits consisting of superconducting qubits and resonators - presently one of the
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conduct research on the topic. Have programming skills (e.g., Python) and experience with numerical simulations or quantum software frameworks (e.g., Qiskit and PennyLane). Have a demonstrated ability to
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advantage. Good data-analysis and programming skills (e.g., Python, MATLAB, or similar). A hands-on approach to experiments, patience in troubleshooting, good communication skills, and the ability to
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numerical skills especially in Matlab, Mathematica, Python or similar programming languages. Previous research experience with toolkits for quantum computing such as Qiskit, QuTiP, PennyLane, Quil, etc
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generative models. Programming skills suitable for modern machine learning research, preferably including Python and deep learning frameworks. Interest in one or more of the following areas: speech synthesis