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PhD in 2D spintronics theory in Regensburg (No replies)
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We are excited to announce the availability of a PhD position in Condensed Matter Theory in the Physics Department of the University of Regensburg. The position is hosted by the Spintronic Research Group:
https://www.uni-regensburg.de/physics/fabian/spintronics/index.html
The successful candidate will be part of a dynamic research team focusing on fundamental aspects of spin phenomena in 2D materials and van der Waals heterostructures. This research will involve a combination of analytical and computational methods (first principles and AI processing) providing an opportunity to contribute to cutting-edge developments in the field.
The applicants should have a Master’s degree (or equivalent) in theoretical physics, preferably specializing in condensed matter physics, and demonstrated proficiency in theoretical and computational techniques.
Shortlisted candidates will be invited for an interview, and the successful applicant is expected to commence their Ph.D. research training by Fall 2024.
We look forward to welcoming a dedicated and enthusiastic researcher to contribute to our vibrant research environment. We welcome applications from individuals of diverse gender and race backgrounds.
The cover letter and supporting documents (CV, contact details of two referees) should be addressed to Jaroslav Fabian at [email protected].
The University of Regensburg is located in the picturesque city of Regensburg (Bavaria, Germany), whose UNESCO World Heritage status adds to the allure of a unique blend of academic excellence in a culturally rich and visually captivating environment.
Fabian's group conducts research in condensed matter theory, with a primary emphasis on advancing the field of spintronics and exploring the physics of two-dimensional (2D) materials. The work of the group focuses on the fundamental aspects of the electronic structure, transport, optics, magnetism, and superconductivity, employing modern first principles methodologies as well as phenomenological modeling.