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 favorable muon stopping sites. We do not expect candidates to be experts in all these techniques at the start of the PhD; training and learning will be an integral part of the project.
A key component of the project is translating 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 collaborating in an interdisciplinary environment and are eager to merge methodological development with real scientific applications.
Requirements for candidates include:
You will be based at the Paul Scherrer Institute (PSI) in the "Materials Software and Data" group of Dr. Giovanni Pizzi and will work in close collaboration with Prof. Dr. Titus Neupert at the University of Zürich (UZH) and Prof. Dr. Nicola Spaldin at ETH Zürich, where you will be enrolled in the doctoral program in Materials Science. The doctoral studies include coursework at ETH Zürich 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 underrepresented groups.
Our institution is based on interdisciplinary, innovative, and dynamic collaboration. You will benefit from systematic on-the-job training, personal development opportunities, and our strong vocational training culture. If you wish to optimally balance work and family life or other personal interests, we are able to support you with our modern employment conditions and on-site infrastructure.
For further information, please contact Dr. Giovanni Pizzi at giovanni.pizzi@psi.ch.
Apply online using the form below. Only applications matching the job profile will be considered.
Paul Scherrer Institute, Human Resources Management, Serdal Varol, 5232 Villigen PSI, Switzerland
Location : Villigen PSI
Country : Switzerland