The subject of Applied Physics (https://www.ltu.se/en/research/research-subjects/applied-physics) is part of the Division of Materials Science. We perform research within atomistic modeling, electron structure theory, condensed matter theory, astrophysics and fundamental theoretical physics.

The research group you will be part of focuses on simulations of materials and material growth using atomistic and electronic structure theory methods. We use density functional theory modeling (DFT) as our standard model to describe materials and their properties, but also to develop atomistic models that can describe dynamical changes in the materials over time, so called molecular dynamics (MD). As a member of this research group you will have the possibility to be a part of a dynamic and international team and perform research in close contact with other theory and experimental groups.

Subject Description
The subject comprises physics with an emphasis on calculations and simulations that are closely and widely related to applications and applied research.

Project Description
In this project we will extract affordable potentials from expensive DFT results through machine learning (ML) to accelerate nanomaterial growth in MD simulations. Such interactomic potentials are also called force fields (FF). We have developed a ML-FF for carbon and iron that can be used to model the catalytic growth of carbon nanotubes, which we want to extend to include more elements, like hydrogen, oxygen, nitrogen, as well as other metals (copper, nickel, rutenium, platinum, …). This project involves using DFT to construct datasets of labelled atomic configurations, which will be used to train ML-FFs for MD simulations. Your research will for example target growth mechanisms and fundamental properties associated with nanomaterials. Based on these insights you will predict novel catalysts for controlled growth to achieve better quality products.

Duties
As a PhD student you are expected to perform theoretical work within your research studies as well as communicate your results at national and international conferences and in scientific journals. Most of your working time will be devoted to your own research studies. Project planning and independently developing the research topic are also an important part of your work. In addition, you can have the opportunity to teach.

The project will focus on performing DFT calculations to construct datasets of labelled atomic configurations used to train ML-FFs for MD simulations. The MD simulations will be used to understand nanomaterial growth in so-called chemical vapor deposition (CVD) with regard to catalyst activity, defect formation and healing, and product steering.

Qualifications
You have a Master’s degree in engineering physics, physics, chemistry, materials science or a related subject. Your duties require excellent knowledge in at least 1 of the following subjects: computation of nanomaterials, quantum physics, solid state physics, atomic and molecular physics. Experience in atomistic modelling and machine learning is of advantage. Good command of English is mandatory.

We are looking for a student who are enthusiastic about modelling and are able to work both independently and as a part of a team. Your project combines materials physics and machine learning and you will work in close collaboration with experts in the field. So good communication skills and the ability to work interdisciplinary are important.

Further Information
Employment as a PhD student is limited to 4 years, teaching and other department duties may be added with max 20%. You will be placed in Luleå. Start: upon agreement.

For additional information you are welcome to contact Prof. Andreas Larsson, Applied Physics, + 46 920-49 1848, andreas.1.larsson@ltu.s

Union representatives:
SACO-S Kjell Johansson, +46 920-49 1529, kjell.johansson@ltu.se
OFR-S Lars Frisk, +46 920-49 1792, lars.frisk@ltu.se

In case of different interpretations of the English and Swedish versions of this announcement, the English version takes precedence.

Application
We prefer that you apply for this position by clicking on the apply button below. The application should include a CV, personal letter and copies of verified diplomas from high school and universities. Mark your application with the reference number below. Your application, including diplomas, must be written in English or Swedish.

Deadline for application: 2 May 2024
Referens number: 1401-2024