PhD Student in Quantum-Mechanical Simulations of Muons in Materials

PhD Student in Quantum-Mechanical Simulations of Muons in Materials

The Paul Scherrer Institute PSI is the largest research institute for natural and engineering sciences within Switzerland. 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.

Therefore, about one quarter of our staff are post‑docs, post‑graduates or apprentices. Altogether, PSI employs 2,300 people.

This position is part of Muoniverse, a Swiss National Centre of Competence in Research (NCCR) dedicated to advancing muon science across particle physics, quantum materials, and applications ranging from energy research to cultural heritage.

Muoniverse brings together 30 research teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development for PhD students and postdocs.

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Muoniverse positions often serve as bridges between individual research groups and institutions, supported through dedicated measures. We are seeking candidates who thrive in such collaborative environments, enjoy connecting people and ideas across disciplines, and are comfortable working within networked structures. Your ability to contribute to a culture of openness and shared progress is as important as your technical expertise.

Muoniverse is committed to promoting equal opportunities and diversity in science. It actively works towards a diverse scientific community and an inclusive work environment.

This project combines first‑principles simulations based on density functional theory (DFT) with the development of automated and reusable computational workflows for muon studies in materials. The goal of the project is to develop and apply advanced first‑principles methodologies to determine muon stopping sites and muon‑induced effects in materials, explicitly accounting for the quantum nature of the muon.

Building on state‑of‑the‑art DFT workflows for such simulations, you will extend existing approaches beyond classical treatments, incorporating quantum effects and modern data‑driven techniques. Starting with DFT‑based calculations of muon stopping sites and migration pathways, including nudged elastic band (NEB) calculations, you will explore quantum treatments of the muon using approaches such as path‑integral molecular dynamics (PIMD) and/or the stochastic self‑consistent harmonic approximation (SSCHA).

You will further investigate the use of machine‑learned interatomic potentials (MLIPs) to efficiently capture muon–material interactions and enable simulations at an affordable computational cost.

Depending on interests and project evolution, you may also explore generative AI approaches to predict favourable muon stopping sites. Training and learning will be an integral part of the project, so we do not expect candidates to be experts in all these techniques at the start of the PhD.

A key component of the project is also the translation of these methods into robust, reusable, and user‑friendly workflows, enabling their adoption by the broader µSR and materials‑science communities. This includes contributing to and extending existing AiiDA‑based workflows and graphical interfaces (e.g. AiiDAlab Quantum ESPRESSO applications) for automated muon simulations and analysis.

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. You have experience working independently but also enjoy working in an interdisciplinary and collaborative environment, and are eager to combine methodological development with real scientific applications.

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