PhD Student in Electronic-Structure Machine Learning Materials

The Paul Scherrer Institute PSI is Switzerland's largest research institute for natural and engineering sciences. We perform cutting‑edge research in the fields of future technologies, energy and climate, health innovation and fundamentals of nature. By performing fundamental and applied research, we work on sustainable solutions for major challenges facing society, science and economy. PSI is committed to the training of future generations, with about one quarter of our staff being post‑docs, post‑graduates or apprentices.

Altogether, PSI employs 2300 people.

PhD Student in Electronic‑Structure Machine Learning for Materials (Index‑‑28526).

• Contribute to the co‑development of transferable e‑ML models, investigating the interplay between model design, training strategies, computational efficiency, transferability, and predictive accuracy across a broad range of materials systems.
• Generate and curate high‑quality electronic‑structure datasets using automated and reproducible AiiDA‑based workflows for model training and benchmarking.
• Validate and benchmark the predictive performance of the models for advanced materials properties beyond standard band structures and charge densities, including electron–phonon coupling and operators and observables related to Berry phases and other electronic‑structure quantities.
• Explore the development of transferable foundation models for materials applicable across the periodic table.
• Contribute to the development of robust, reusable, and efficient open‑source software and workflows, integrating machine‑learning frameworks with established electronic‑structure codes.

We are looking for a highly motivated candidate with a background in computational materials science or condensed‑matter physics, and a keen interest in developing and applying advanced simulation methods and implementing them in workflows. Candidates should be able to work independently, enjoy collaboration in an interdisciplinary environment, and be eager to combine methodological development with real scientific applications. Training and learning will be an integral part of the project.

• Master’s degree (or close to completion) in physics, materials science, chemistry, engineering, or a closely related field.
• Hands‑on experience using density functional theory (DFT) for research or projects, and/or experience in developing machine‑learning (ML) models applied to materials.
• Working knowledge of Python for scientific computing and data analysis.
• Comfortable communicating research ideas and results in English, both in writing and in conversation.
• Interest in quantum simulations, modern machine‑learning models, the development of new computational methods, and/or materials modelling.

You will be fully based at the Paul Scherrer Institute PSI in the Materials Software and Data group of Dr Giovanni Pizzi, and work in close collaboration with the group of Prof Dr Michele Ceriotti  doctoral studies include coursework at EPFL and may involve teaching duties. Results obtained during the PhD are expected to be published in peer‑reviewed journals and presented at international conferences.

We are convinced that our research team functions best when it is maximally diverse, and we particularly encourage applications from members of under‑represented groups.

Our institution is based on an interdisciplinary, innovative and dynamic collaboration. You will benefit from systematic training on the job, in addition to personal development possibilities and pronounced vocational training culture. Our modern employment conditions support work‑family life balance.

Please submit your application online by 21 June 2026, including a one‑page cover letter, your CV, transcript of records, and contact details for two referees.

Contact:
Dr Giovanni Pizzi, giovanni.pizzi.