Predicting materials properties by quantum Monte Carlo and machine learning techniques
For reliable materials simulations, it is becoming of paramount importance to achieve both high quality electronic description and large simulation sizes, from systems of biochemical interests (water, ice and aqueous systems), planetary science (materials under high-pressures such as compressed hydrogen, water, methane, iron), glasses and alloys, to name a few. One of the main tasks of the proposed project is to develop quantum Monte Carlo techniques for state of the art electronic simulations, that can be used for understanding emergent properties in correlated materials (e.g. HTc superconductors, excitonic, Mott and topological insulators) and for obtaining force fields of ab initio quality with machine learning (ML) techniques.
The interested applicant should have a strong motivation to discover new effects that may be driven by strong electron correlation and that cannot be detected by standard techniques based on density functional theory.
The candidate should also have excellent skills in developing codes and algorithms.
The position is for three years, with formal renewals each year.
The net salary is about 2500euro/months with extra funds available for traveling and international collaborations.
Interested applicants should send a CV, list of publications, research interests, and at least two reference letters (sent by the supervisor or qualified
Dated: October 9, 2019