We are looking for a Doctoral Researcher for Quantum-inspired tensor network machine learning solvers for super-moiré van der Waals materials.
Moiré van der Waals superlattices provides an ideal platform to engineer emergent quantum states, including correlated and topological phases. Super-moiré structures enable hierarchical length scales and quasiperiodic potentials, requiring modeling techniques capable of handling extremely large system sizes. In this project, we develop algorithms based on active-learning tensor-network tight-binding strategies that enable simulations of ultra-large aperiodic systems at unprecedented scales, both in equilibrium and out of equilibrium. The project will enable us to study collective modes, including excitons and plasmons, in topological and correlated states in super-moiré van der Waals heterostructures. Milestones include the development of real-space topological probes using the tensor-network formalism, the computation of collective quasiparticle excitations in super-moiré with tensor networks, the computation of transport in super-moiré systems, and the development of a quantum-inspired active Hamiltonian-learning from real-space spectroscopy for super-moiré systems. This interdisciplinary approach combines tensor-network frameworks, machine learning, and STM spectroscopy to enable scalable quantum material design.
Further information and application procedure at (deadline May 17th 2026)https://www.aalto.fi/en/open-positions/doctoral-researcher-in-quantum-inspired-tensor-network-machine-learning-solvers-for-super-moire-van
