Postdoctoral Researcher in Machine Learning for Light-Induced Excited-State Dynamics

Your role in the team

Our expectations of you

Education

• A PhD in condensed matter physics, or a very closely related field; candidates who have not yet completed their PhD may apply if the doctoral degree will be awarded by the date of signing the employment contract.

Qualifications

• A strong background in physics.
• Strong knowledge of quantum mechanics and of the theory of electronic excitations in materials.
• Solid understanding of light-matter interaction and of first-principles methods for excited states and their dynamics (e.g., TDDFT, nonadiabatic molecular dynamics).
• Demonstrated ability to carry out independent research, evidenced by publications in peer-reviewed journals.
• Good command of English, both written and spoken.
• Knowledge of electron-phonon coupling, nonadiabatic dynamics, and non-equilibrium phase transitions in photoexcited materials.

Experience

• High-level programming skills (e.g., Python, C/C++, Fortran) and experience with modern machine-learning frameworks (e.g., PyTorch).
• Demonstrated experience in the development of machine-learning methods and models for physical or materials problems. We are looking for researchers who develop methods, codes, and models - not users. Experience limited to running standard codes or training simple ML models will not be considered.
• Experience with real-time TDDFT simulations of solids under laser excitation.
• Experience with the development of machine-learning interatomic potentials or machine-learned electronic Hamiltonians, e.g., based on graph neural networks, and their extension to excited states.
• Erfahrung mit groß angelegten ab initio und maschinellem Lernen gesteuerten Molekulardynamik-Simulationen.
• Erfahrung mit Hochleistungsrechenumgebungen.
• Experience in collaborating with experimental groups (e.g., ultrafast spectroscopy, time-resolved diffraction).

What we offer