The Condensed Matter Theory Group (I. Néel / CNRS) in Grenoble, France invites applications for a postdoctoral position in the theory of topological states of matter.  The position is oriented to develop theoretical modelling of ongoing experiments and devices in topological metals and insulators within the SCHINES collaboration (https://schines.cnrs.fr/).

Application Deadline : 10 December 2021

Le but de ce stage théorique est d’étudier à l’aide d’une approche de type de Kubo-Greenwood les effets de la dimensionnalité du système considéré sur la nature du transport quantique électronique dans des systèmes présentant une ou de multiples bandes plates (non dispersives) dans leur structure de bande. Dans un second temps, on introduira du désordre dans le système (lacunes et potentiels aléatoires sur site) pour évaluer l’impact de ce dernier sur les propriétés de transport. On envisage par ailleurs de relier ces grandeurs physiques étudiées aux propriétés topologiques des états quantiques du système.

The discovery of superconductivity in nickelates has led to one of the most active topics of research in condensed matter physics in the last two years (see e.g. [1]). These systems open new exciting perspectives to eventually understand the challenge of unconventional superconductivity.

The goal of the internship is the theoretical investigation of novel nickelate superconductors by means of first principles calculations (density functional theory). These calculations will be oriented towards the subsequent incorporation of many-body effects (electronic correlations), which are believed to play a key role in the physics of these unconventional superconductors.

[1] Nickelate superconductors: an ongoing dialog between theory and experiments A. S. Botana, F. Bernardini, and A. Cano, JETP 159, 711 (2021); arXiv:2012.02764

Machine learning methods are new tools, whose possibilities reserachers are intensively exploring over the last 2-3 years. The main objective of this intership will be to explore the possibility to predict magnetic interactions using deep learing methods. This is a theoretical subject aiming at exproring the possibilities offered by a new domain. The student will thus have to learn deep learning methods, but also the physics underlying the effective magnetic interacions in a material and the ab-intio methods able to accurately evaluate them.

The student should work with our collaborators in Deep Learning (from SIMAP, Grenoble), in magnetism and ab-initio calculations (from ILL), as well as quantum chemists from Poitiers University and experimental chemists from Lyon whether this internship will foster a PhD.