PhD Student in Electronic-Structure Machine Learning for Materials
This PhD project is part of the new Swiss project "Learning the electrons: Design, training and application of a general model of the electronic structure of matter" and aims to develop next-generation machine‑learning models for electronic‑structure theory.
The project combines developments in machine learning, quantum‑mechanical simulations and scientific software infrastructure. It will investigate design, training and validation of transferable electronic‑ML (e‑ML) models across a broad range of materials systems and develop robust open‑source software and workflows.
Your tasks
* Contribute to the co‑development of transferable e‑ML models, investigating the interplay between model design, training strategies, computational efficiency, transferability, and predictive accuracy.
* Generate and curate high‑quality electronic‑structure datasets using automated 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.
Your profile
We are looking for a highly motivated candidate with a background in computational materials science or condensed‑matter physics, with a keen interest in developing and applying advanced simulation methods and implementing them in workflows. Candidates should be able to work independently and enjoy interdisciplinary collaboration. Training and learning will be an integral part of the project.
Requirements for candidates
* 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 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, development of new computational methods, and/or materials modeling.
Benefits
You will be full‑time based at the Paul Scherrer Institute PSI in the Materials Software and Data group of Dr Giovanni Pizzi and work closely with Prof Dr Michele Ceriotti at EPFL. The PhD will include coursework at EPFL, teaching duties, publication in peer‑reviewed journals and presentation at international conferences. Modern employment conditions and on‑site infrastructure support work–life balance and personal development.
Equal Opportunity
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.
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