Positions for Research Assistants or Associates
Last changes 23.01.2022
We occasionally have new PhD positions coming up. Please contact Prof. Hongbin Zhang for possible open positions.
Currently there is an open position:
Open ARL/ Master Thesis Positions
We are looking for students to do a research project (advanced research lab or master thesis) within our group in the following areas listed below.
If you are interested, please contact Prof. Dr. Hongbin Zhang to discuss possibilities.
IMPORTANT for all positions: we use python, so please get yourself prepared for it, at least spiritually.
In this project, we are going to design novel functional van der Waals materials, i.e. heterostructures of 2D materials with different functionality to achieve mutual control of different order parameters.
In this project we will try to optimize the magnetic properties by interstitial and substitutional doping. With the help of high throughput calculations, the thermodynamic stability of a vast spectra of structural variations will be evaluated to identify the site occupation preference, followed by explicit evaluation of the magneto crystalline anisotropy.
Spin-gapless semiconductors (SGS) are half metals with the majority spin channel being semi-metallic, i.e., the gap is zero, while there is a finite band gap in the minority spin channel. Recently, we have performed high throughput screening for 3D SGSs in quaternary Heusler compounds. In this project further systematic calculations on more compounds with the Heusler or other structures will be carried out, followed by transport and topological characterization, surface and interface effects.
In this project, we will focus on magnetic materials with kagome lattices and try to understand how the topological properties can be engineered in such compounds. Various aspects will be explored such as anomalous Hall/ Nernst conductivity, spin Hall conductivity, topological orbital moments, nonlinear optoelectronic properties, and possible topological properties of the magnonic excitations.
With the development of machine learning in particular deep learning techniques functional materials are inversely designed. We have recently successfully developed a method to transform the known crystal structures in the crystal graphs and have implemented a generative adversial network model for predicting novel crystal structures. In this project the magnetization data should be tried to be incorporated into the deep learning model so that stable magnetic materials will be predicted.
In this project we are going to focus on establishing a methodology to determine the magnetic ground state from exchange interactions, accounting for how difference reference states (FM, AFM, PM) and what terms to include in the Hamiltonian (such as spin-orbit coupling), thus laying the ground work for a comprehensive description of the ground state within computational material design.
In this project, we are going to perform massive density functional theory calculations to evaluate the thermal conductivities for both 2D and 3D insulators with large band gaps, with a particular focus on those cases with tunable structural phase transitions. If time allows, we will also carry out explorative calculations to get the interfacial thermal resistance.
In this project, we will set out to discover new compounds of Fe2P-type family, along with doping mechanisms to optimize their magnetic-caloric effect (MCE) based on density functional theory calculations. The codes and workflows have been developed and tested on other materials systems in the group, where the stability, electronic structure and finite temperature magnetic properties will be evaluated.
