The most promising path towards leading large-scale quantum computers is via topological quantum computing. Here computational errors are minimized by encoding logical qubits in several physical qubits, which are kept in highly entangled quantum states either using a periodic sequence of measurements (as in IBM's and Google's quantum computer prototypes) or by the use of special, topological excitations, the so-called Majorana zero modes (as in the approach favoured by Microsoft). Quantum gates, the basic elements of quantum computation, are then supposed to be performed on these encoded qubits. Even in a topological quantum computer, the maintenance of encoded qubits requires some active error correction, especially in IBM's approach, which can benefit from the use of  machine learning. The PhD student would do research on topological quantum computing, such as machine-learning-assisted correction of correlated qubit errors, and explore how machine learning or other approaches can be applied to Majorana zero mode quantum computing schemes.