Postdoc in Machine Learned Semiconductor Material Properties 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 Qua Tr Ex  developers.

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. The aim is to predict these properties for arbitrarily large structures, at a DFT-level of accuracy. As a starting point, you will extend the large-scale equivariant GNNs we develop for Hamiltonian matrix prediction to treat dynamical matrices.

This ML framework will then also allow us to produce the derivatives of both quantities, which correspond to the electron-phonon and anharmonic phonon-phonon coupling elements. With these, dedicated scattering rates can be computed and then used in quantum transport simulations. Down the line, we aim to pre-train a common GNN backbone model capable of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency.

There will be a significant computational component in deploying multi-GPU codes to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest. Your contributions would be across the spectrum from methodological development, implementation, and application to realistic semiconductor device systems made of thousands of atoms. All codes will be made freely available to the scientific community through Git Hub.

• A track record in building and deploying ML models for applications in materials research, and willingness to work on both methods development and applications
• Publications in top ML conferences and/or prominent journals in materials sciences and device physics
• Enjoy collaborating with other researchers in a friendly environment
• Be willing to supervise junior PhD and master students

• Your job with impact:
  Become part of ETH Zurich, which not only supports your professional development, but also actively contributes to positive change in society
• You can expect numerous benefits, such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ, childcare and attractive pension benefits
• We offer an exciting and challenging activity in a team of highly motivated physicists, electrical engineers, and computer scientists and a salary according to the standard of ETH Zurich for post-docs
• The duration of the post-doc can be up to two years
  . The participation in international conferences and the collaboration with industry and academia is strongly encouraged and supported

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