PhD Researcher in Computational Nanoelectronics (Remote, Denmark)

Job summary

A PhD position is available focusing on theoretical nanoelectronics through predictive first principles calculations. The role involves modeling electron transport in interfaces formed by twisted layers of ultra-thin oxide membranes and two-dimensional van der Waals materials, with an emphasis on understanding the properties influenced by twist angles.

Qualifications

• A two-year master's degree (120 ECTS points) or equivalent academic level.
• Strong background in theoretical or computational condensed matter physics.
• Experience in scientific programming and atomistic simulations.
• Interest in Density Functional Theory (DFT), electron transport, and machine learning.
• Ability to collaborate effectively and communicate scientific results in English.

Responsibilities

• Predict atomic and electronic structures at interfaces using DFT.
• Conduct electron transport calculations integrating DFT with non-equilibrium Green's function methods.
• Apply Machine Learning techniques to enhance DFT for larger, experimentally relevant systems.
• Develop computational workflows for screening calculations based on varying parameters like twist-angle and voltage bias.
• Collaborate with project partners to interpret experimental findings.
• Contribute to the development of open-source tools.

Skills

• Proficiency in DFT and NEGF methods.
• Familiarity with machine learning applications in physics.
• Strong programming skills for scientific computing.

Education

• A master's degree in a relevant field is required.

Tools

• Density Functional Theory (DFT)
• Non-equilibrium Green's function (NEGF) methods
• Machine Learning (ML) techniques