PhD Candidate in Computational Nanoelectronics

Job summary

A PhD position is available focusing on theoretical nanoelectronics through predictive first principles calculations. The role involves modeling and simulating electron transport in interfaces formed by twisted layers of ultra-thin oxide membranes and two-dimensional van der Waals materials, which exhibit significant moiré patterns and tunable properties based on twist angles.

Qualifications

• A two-year master's degree (120 ECTS points) or equivalent academic level.

Responsibilities

• Predict atomic and electronic structures at interfaces using Density Functional Theory (DFT).
• Conduct electron transport calculations integrating DFT with non-equilibrium Greens function methods.
• Apply Machine Learning (ML) techniques to enhance DFT applications to larger experimental systems.
• Develop computational workflows for screening calculations involving twist angles, voltage bias, and gate potential.
• Collaborate with project partners to interpret experimental findings.
• Contribute to the development of open-source computational tools.

Skills

• Strong background in theoretical or computational condensed matter physics.
• Experience in scientific programming and atomistic simulations.
• Interest in DFT, electron transport, and machine learning.
• Ability to communicate scientific results effectively in English.

Education

• A master's degree or equivalent in a relevant field.

Tools

• Density Functional Theory (DFT), Non-equilibrium Greens function methods, Machine Learning (ML) techniques.