Postdoc in Machine Learned Semiconductor Material Properties for Quantum Transport Simulations

Project background

The Computational Nanoelectronics Group was recently awarded a grant from the Swiss National Science Foundation entitled Machine Learning for Optimized Ab-initio Quantum Transport Simulations (MALOQ). It officially started on January 1st 2026 and will conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical quantities that enter ab-initio QT simulations of nanoelectronic devices. In this context, we are seeking a post-doctoral fellow who will be part of a team that also comprises two PhD students and will closely collaborate with the QuaTrEx developers.

Job description

As part of the MALOQ project, you will train state-of-the-art ML models to learn atomic, electronic, and vibrational properties of large-scale atomic systems representing the building blocks of semiconductor devices. T...