PhD Student in Electronic-Structure Machine Learning for …, Villigen

PhD Student in Electronic-Structure Machine Learning for Materials The Paul Scherrer Institute PSI is a leading research centre in Switzerland, conducting cutting‑edge research in future technologies, energy, climate, health and fundamental science. This PhD project is part of the Swiss project “Learning the electrons”, which aims to develop next‑generation machine‑learning models that predict electronic structure and advanced material properties with high accuracy and efficiency.

Responsibilities

Co‑develop transferable e‑ML models, exploring model design, training strategies, computational efficiency, 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 Berry phase‑related operators.

Explore the development of transferable foundation models for materials that can be applied 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.

Your profile 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 in workflows. The successful applicant will be able to work independently, enjoy interdisciplinary collaboration, and combine methodological development with real scientific applications. Training and learning are integral parts of the project, so experts in all techniques are not required from the outset.

Requirements

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 developing machine‑learning 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, and the development of new computational methods and materials modelling.

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.

Benefits Our institution offers a dynamic collaboration environment and systematic training on the job, in addition to personal development opportunities and a pronounced vocational training culture. Modern employment conditions and on‑site infrastructure support optimal work‑life balance.

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