Ph3PhD Student in Electronic-Structure Machine Learning for Materials /h3 pThis 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. /p pThe 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. /p h3Your tasks /h3 ul liContribute to the co‑development of transferable e‑ML models, investigating the interplay between model design, training strategies, computational efficiency, transferability, and predictive accuracy. /li liGenerate and curate high‑quality electronic‑structure datasets using automated AiiDA‑based workflows for model training and benchmarking. /li liValidate 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. /li liExplore the development of transferable foundation models for materials applicable across the periodic table. /li liContribute to the development of robust, reusable, and efficient open‑source software and workflows, integrating machine‑learning frameworks with established electronic‑structure codes. /li /ul h3Your profile /h3 pWe 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. /p h3Requirements for candidates /h3 ul liMaster's degree (or close to completion) in physics, materials science, chemistry, engineering, or a closely related field. /li liHands‑on experience using density functional theory (DFT) for research or projects, and/or experience in developing machine‑learning models applied to materials. /li liWorking knowledge of Python for scientific computing and data analysis. /li liComfortable communicating research ideas and results in English, both in writing and in conversation. /li liInterest in quantum simulations, modern machine‑learning models, development of new computational methods, and/or materials modeling. /li /ul h3Benefits /h3 pYou 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. /p h3Equal Opportunity /h3 pWe are convinced that our research team functions best when it is maximally diverse, and we particularly encourage applications from members of under‑represented groups. /p /p #J-18808-Ljbffr