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for interdisciplinary research at the interface of energy systems, high-performance computing, and quantum computing Strong background in mathematics, optimization, numerical methods, or scientific
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in a way suitable for quantum algorithms software implementation of algorithms and numerical investigations using our high-performance computing system finding ways to increase the efficience of these
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quantum annealing in particular. experience in implementing and running quantum software using Qiskit or similar. experience with cluster-based simulation or high-performance computing. Personal
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hereafter. The duration of the position is three years. Your work tasks This PhD position focuses on the application of quantum computing to computational challenges in power systems with high shares of
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simulating dynamical quantum systems. experience with cluster-based simulation or high-performance computing. Personal qualities We are looking for applicants who: are able to work autonomously
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trade-offs between predictive performance, computational efficiency and scalability, using high-performance and cloud computing environments. Depending on the agreed research direction, you may also
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nanofabrication, nanoelectronics, atomic force microscopy, quantum sensing and computing, high speed optical devices and atomistic device simulations. PhD: 3-4 years full-time; 6-8 years part-time; Thesis of
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-temperature scanning tunnelling microscopy, ARPES, Kerr microscopy, femtosecond pump-probe and terahertz spectroscopy, electron microscopy, positron annihilation. Theory: high-performance computing, and
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operation and very low energy consumption. The overall aim is to establish new device concepts that could contribute to high-performance classical computing and emerging brain-inspired computing hardware
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central challenges on the path toward large-scale quantum computing. In this PhD project, you will investigate how machine learning can enable faster, more scalable QEC decoding . The goal is to develop new