In order to fill a fixed-term position in full-time (39.83 hours/week = 100%) at the earliest possible date, we are looking for 1
Postdoctoral Researcher (m/f/x) in Machine Learning for Light-Induced Excited-State Dynamics in Materials
The successful applicant will join the research group of Prof. Silvana Botti at the Research Center Future Energy Materials and Systems (RC FEMS) and the Ruhr University Bochum (RUB). The group develops first-principles and machine-learning methods to describe electronic excitations, light–matter interaction, and ultrafast dynamics in materials and at functional interfaces. The newly founded Research Center focuses on developing innovative materials and systems for sustainable energy applications. The center’s research areas include photovoltaics, thermoelectricity, energy storage, fuel cells, and sustainable chemical processes, among others. Its interdisciplinary approach involves collaborations between physicists, chemists, engineers, and material scientists to address the challenges of transitioning to a low-carbon economy. The center also offers opportunities for graduate students and postdoctoral researchers to participate in cutting-edge research projects and receive advanced training in the field of energy materials and systems. The chair of Prof. Botti is affiliated with the Faculty of Physics and Astronomy of the Ruhr University Bochum. This faculty is a leading research and teaching institution in the fields of experimental and theoretical physics and astronomy, with a focus on condensed matter physics, astrophysics, and particle physics.
We are seeking a highly motivated postdoctoral researcher (m/f/x) with a strong background in condensed matter physics and a proven record in developing machine-learning methods. The position focuses on the development of new computational approaches that combine real-time time-dependent density functional theory (rt-TDDFT), nonadiabatic excited-state dynamics, and machine learning, in order to reach the time and length scales needed to describe light-induced phenomena in complex materials.
Scope: full-time
Duration: fixed-term, 3 Years
Start: at the earliest possible date
Apply by: 2026-10-06
Your tasks:
The successful candidate (f/m/x) will
- develop and apply methods at the intersection of real-time TDDFT, nonadiabatic excited-state molecular dynamics, and machine learning
- design and implement machine-learning models for photo-excited systems (e.g., excited-state interatomic potentials, machine-learned Hamiltonians, models for the time evolution of electronic states), trained on and validated against first-principles data
- investigate ultrafast light-induced processes in materials, such as photoinduced structural phase transitions, coupled electron–phonon dynamics, and non-equilibrium carrier and lattice dynamics, including materials and interfaces relevant for energy applications
- publish results in peer-reviewed journals, present them at international conferences, and collaborate with theoretical and experimental partners within RC FEMS and beyond
Your profile:
Required qualifications:
- 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
- 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)
- 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.
- Demonstrated ability to carry out independent research, evidenced by publications in peer-reviewed journals
- Good command of English, both written and spoken
Desirable qualifications:
- 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
- Knowledge of electron–phonon coupling, nonadiabatic dynamics, and non-equilibrium phase transitions in photoexcited materials
- Experience with large-scale ab initio and machine-learning-driven molecular dynamics
- Experience with high-performance computing environments
- Experience in collaborating with experimental groups (e.g., ultrafast spectroscopy, time-resolved diffraction)
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