Teaching


winter 2024/2025

summer 2024



online courses | traditional lectures | chronological | seminars | M-Lab




online courses

Computational Physics

An undergraduate course providing an overview of numerical algorithms to solve physics problems, with application to classical mechanics, E & M, quantum mechanics, and statistical physics. The course includes an in-depth introduction to programming in JULIA.

[course website, vimeo channel]





Statistical Physics

An undergraduate course introducing a statistical description of nature, using concepts such as entropy, thermal equilibrium, and statistical potentials. Basics of thermodynamics, phase transitions, and non-equilibrium physics.

[course website, vimeo channel]






Computational Many-Body Physics

A graduate level course that provides an overview of modern numerical approaches to many-body systems, both classical and quantum. The in-depth introduction of elementary algorithms includes Monte Carlo methods, machine learning techniques, and entanglement based approaches.

[course website, vimeo channel]








traditional lectures *Albertus-Magnus-Lehrpreis: M-Lab Computational Physics 2023, Computational Physics 2016, Classical Field Theory 2012





chronological

2024: Quantum Computational Physics
2024: Computational Many-Body Physics
2023: Statistical Physics
2023: Computational Physics
2022: Solid State Theory
2021: Vorkurs
2021: Computational Many-Body Physics
2020: Statistical Physics
2020: Computational Physics
2019: Solid State Theory
2019: Computational Physics
2018: Solid State Theory
2018: Computational Many-Body Physics
2017: Statistical Physics
2016: Solid State Theory
2016: Computational Physics
2015: Advanced Quantum Mechanics
2015: Computational Many-Body Physics
2014: Advanced Quantum Mechanics
2014: Computational Physics
2013: Classical Field Theory
2013: Computational Physics
2012: Classical Field Theory
2012: Computational Many-Body Physics





seminars

2023: Quantum Computing: Entanglement, Measurements, and Simulations
2021: Machine Learning Quantum Matter
2020: Dirac Matter and Twisted Bilayer Graphene
2018: Majorana fermions
2016: Disentangling quantum matter via quantum information theory
2015: Dirty physics: disorder effects in quantum matter
2014: Quantum knots: Monopoles, skyrmions, and Majorana fermions in condensed matter systems
2013: Entanglement: From quantum information theory to the classification of quantum matter
2012: Topological states of matter: Concepts, materials, and quantum computers





M-Lab (practical course)

since 2023: M-Lab: Computational Physics