A postdoctoral position at University of Zurich – Institute of Computational Science-  is available in the group of Prof. Guglielmo Mazzola.

The newly established group will focus on understanding matter under extreme conditions using state-of-the art electronic structure methods (quantum Monte Carlo and DFT), and machine learning. The main goal of the project is to develop and apply a machine-learning powered strategy to scale-up computationally prohibitive first-principle simulations and provide a new quantitative understanding of the thermodynamics of dense materials for technological and planetary science applications.

The project is interdisciplinary and will benefit from high-level collaborations with other experts in numerical methods for strongly correlated electrons, machine learning for atomistic simulations, and planetary science. Side-projects at the interface of statistical mechanics, machine learning, and quantum simulations are also possible.

The interested applicant should have a strong background in condensed matter and statistical physics as well as excellent skills in developing theory, code, and algorithms. The successful candidate has a demonstrated record of theory and algorithmic developments in (i) electronic structure methods (ideally beyond standard DFT), OR (ii) machine learning methods for physics and chemistry application (not limited to ML enhanced atomistic simulations).

The Institute for Computation Science at UZH is an interdisciplinary center promoting all fields of numerical simulations, data science, and high-performance computing. The city of Zurich offers a very dynamic and international living environment and is consistently ranked as one of the world's best city for quality of living by the Mercer survey.

We offer a stimulating work environment including the possibility to establish high-profile collaborations within the Swiss and international scientific network, as well as a competitive salary. The position is for two years, with extra funds available for traveling and collaborations.

Interested applicants should send their application documents as one pdf document per e-mail to the following address: ([email protected] ).

The document must include (i) a cover letter (max 1 page), (ii) a short research statement where career achievements are discussed (max 1 page), (iii) CV with list of publications. The candidate should arrange to have at least three letters of reference sent on their behalf to the same address. Applications will be considered until the position is filled.