Strong electronic correlations lead to some of the most fascinating phenomena in materials such as (quantum) criticality, high-temperature superconductivity, and entanglement. They open the door for innovations in solar cells, transistors, thermoelectrics, data storage, etc. The underlying many-electron problem, however, is extremely challenging for theory.
We have an opening for positions on three research topics in this area:
Quantum field theory for solids. Diagrammatic extensions of dynamical mean field theory (DMFT) have been a breakthrough for calculating materials with strong electronic correlations including local and non-local correlations. They allow us to describe, among others, pseudogaps and superconductivity induced by antiferromagnetic fluctuations, quantum critical points, and to discover the “pi-ton”, a new type of polariton formed in correlated metals. The aim of the project is to explore new physics on the three-particle vertex level, the next rung on the ladder of approximations.
Artificial intelligence and big data. The aforementioned diagrammatic extensions of DMFT require the storage and manipulation of huge objects (up to many TBytes in size), curtailing the method to smaller systems.One objective is to use tensor networks and machine learning algorithms for pattern recognition and data compression which will push the algorithms to a completely new computational level. The second objective is to use machine learning to select the relevant Feynman diagrams out of the myriad of partially canceling ones.
Realistic materials calculations. Density functional theory plus dynamical mean field theory (DFT+DMFT) is a state-of-the-art method for calculating materials with strong correlations such as transition metal oxides, heterostructures thereof, and f-electrons systems. Given the recent recent discovery of superconductivity in nickelates following our predictions, we will revisit nickelate heterostructures against the background of the new experiments using DFT+DMFT, and later the ab initiodynamical vertex approximation.
A strong background in either scientific computing or quantum field theory is necessary depending on the research topic. For the postdoctoral position, familiarity with one or several of the aforementioned methods is of advantage.
Vienna, a vibrant city close to the alps with a wide array of cultural offerings, has been ranked by Mercer and the Economicst as the city with the highest quality of life in the world for multiple years now.
TU Wien is committed to increase the percentage of women. Female applicants are explicitly encouraged to apply and will be given preference when equally qualified. We also explicitly encourage handicapped persons with appropriate qualifications to apply.
Please send your CV including a list of publications, the name and email addresses of two referees, and your preference regarding the three research topics to firstname.lastname@example.org.
Additional Salary Information: Annual salary depends on applicant's level of experience (master's/Ph.D). Salary includes a full benefits package.
About TU Wien - Vienna University of Technology
The TU Wien (Vienna University of Technology) is one of Europe's leading research universities. Its 3,800 scientists, 5,000 staff and 28,000 students are committed to excellence in teaching and research. Its motto "Technology for people" has stood for over 200 years.
The TU Wien provides extensive benefits and is an equal-opportunity employer with an inclusive environment for women and minorities. Vienna, a vibrant city close to the alps with a wide array of cultural offerings, is ranked by Mercer and the Economist as the city with the highest quality of life in the world.
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