Lode Pollet

Lode Pollet is a Professor at the Faculty of Physics, Ludwig Maximilian University of Munich (LMU). His research focuses on Theoretical Condensed Matter Physics, Quantum Monte Carlo Simulations, and Disordered Systems.

Current research topics include

• Machine learning applications to quantum phase characterization
• Spin liquids in centred pyrochlore lattices
• Fracton models for quantum error correction
• Cold atomic systems phenomenology

His work has been supported by prestigious grants:

• Marie-Curie Researcher Integration Grant 'FDIAGMC' (2012-2016)
• ERC Starting Grant 'QUSIMGAS' (2013-2018)
• ERC Consolidator Grant 'QSIMCORR' (2018-2023)

These fundings enabled exploration of quantum phase transitions, topological order, and numerical methods for many-body systems.

Recent publications highlight trends in

• Machine learning for phase diagrams
• Quantum simulation of frustrated magnets
• Fracton models in 3D quantum memory
• Polaron transport in photovoltaics

His group collaborates with institutions like

• University of Würzburg
• University of Massachusetts, Amherst
• Max Planck Institute

and offers PhD positions in Theoretical Condensed Matter Physics.