PhD position in modeling halide perovskites for solar cells

at  Chalmers

Join our team at the forefront of research in condensed matter and materials theory, where you will be integral in advancing the field of solar materials. As a PhD student in our dynamic division of Condensed Matter and Materials Theory, you will have the unique opportunity to engage in the development and understanding of innovative materials, specifically focusing on electronic structure and atomistic modeling of halide perovskites for solar cells.

PROJECT DESCRIPTION

As a PhD student in this role, you will focus on electronic structure and atomistic modeling of halide perovskites with emphasis on effects such as defects, charge localization or interfaces. Your work will be pivotal in advancing our understanding of quantum mechanical processes that occur in for example solar devices. You will use tools based on density functional theory, molecular dynamics, and machine learning. This research holds great potential for breakthroughs in th design of solar cells, directly impacting the future of sustainable energy technologies.
You will be collaborating with both local and international experimental groups, presenting your findings at conferences, and contributing to scientific journals—building your expertise while shaping cutting-edge advancements.

QUALIFICATIONS

You should hold a Master degree or equivalent in physics, chemistry or materials science and have a strong interest in computational modeling and electronic structure calculations. You should enjoy working in a collaborative environment including interactions with both theoreticians and experimentalists
The position requires sound verbal and written communication skills in English. If Swedish is not your native language, Chalmers offers Swedish courses.

• Study the role of polarons, defects and/or interfaces in semiconductor materials, relevant to applications like solar cells and LEDs.
• Perform calculations using density functional theory (DFT) codes such as VASP and CP2K.
• Apply molecular dynamics and machine learning techniques within the project.
• Develop and drive your own projects.
• Collaborate with experimental partners to ensure comprehensive research outcomes.
• Communicate research findings through presentations at international conferences and publications in scientific journals.