Topic: numerical stability

Develop quantum-computing approaches to fluid dynamics. Investigate lattice-Boltzmann formulations, linearisation, sparse-Hamiltonian and quantum linear-systems methods, alongside hybrid classical–quantum approaches. Compare formulations for resource demand, numerical stability, scalability and practical implementation, and help shape the project’s scientific direction. Apply through the ETH online recruitment portal. The vacancy is currently advertised with an active application route; no closing date is stated.

Matrix multiplication underpins scientific computing and artificial intelligence, yet practical numerical libraries and hardware accelerators commonly retain the classical cubic-time algorithm despite decades of subcubic theoretical advances. This talk reviews the effort to make faster multiplication algorithms useful in practice. It examines why arithmetic complexity alone does not determine performance: some algorithms require enormous matrices or incur large hidden constants, while communication costs, numerical stability and the match between software and hardware create additional obstacl