Institute: University College London
Supervisor: Alexander Shluger
Closing date: 31 March 2024

 

About the Project

A 3.5 years PhD position is available in the group of Prof. Alexander Shluger at the Department of Physics and Astronomy, UCL co-funded by Nanolayers Research Computing and Applied Materials Inc. The project will provide new computational methodologies to study mechanisms of fundamental processes governing the dynamic behaviour of interfaces in multilayer metal/oxide devices. It aims to establish the mechanisms of structural, chemical and resistance changes in multi-layered materials of different composition via a combination of electronic and ionic mechanisms, and investigate how electrical bias, humidity and temperature can be used to control the structure and resistivity of oxide layers. This will be achieved using a combination of advanced statistical thermodynamics, molecular dynamics and kinetic Monte Carlo techniques combined with machine learning and data mining.

The main objectives are to: 1) Establish reliable models of complex multilayer materials including up to several layers of amorphous and polycrystalline insulating (ZrO2, TiO2, Al2O3, ZnO, Ga2O3) and conducting (TiN, Ti, Al) components of different composition by combining experiments and computational modelling; 2) Elucidate mechanisms of changes in chemical element distribution, microstructure and conductivity resulting from application of electrical stress; 3) Apply these models and mechanisms to understand the physical principles controlling the structure and resistance of complex materials with reactive interfaces in collaboration with Nanolayers and Applied Materials Inc.

Highly motivated candidates interested in developing advanced computational methods for solving fundamental and applied materials problems are strongly encouraged to apply. Applicants should have a top-level MSci degree or equivalent in Chemistry, Physics, Materials or related subjects. Prior experience with first principles quantum mechanical calculations, molecular dynamics simulations and programming is desirable but not essential. Good undergraduate knowledge of quantum physics and Solid State Physics is essential. You should enjoy coding, scripting and analytics, and be keen to push the boundaries of machine learning and artificial intelligence in materials applications.

Please note that, due to funding restrictions, only students eligible for home fees can receive this studentship. The PhD training and research will be carried out within the stimulating environment of the London Thomas Young Centre.

The closing date for applications is March 31st, 2023. Evaluation of applications will commence immediately, and will continue until the position is filled. Applications and inquiries regarding the vacancy should be made to a.shluger@ucl.ac.uk.