Postdoc in Machine Learned Semiconductor Material Properties for Quantum Transport Simulations
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Postdoc in Machine Learned Semiconductor Material Properties for Quantum Transport Simulations
100%, Zurich, fixed-term
The simulation of electronic devices has a long and successful history of accompanying experimental developments, be it for transistors or memory cells. Nowadays, to be of practical relevance, such technology computer aided design (TCAD) tools should operate at the ab-initio and quantum mechanical level. Moreover, they should capture the interplay between electrical (voltage-induced currents), thermal (excitation of crystal vibrations), and structural (migration of atoms) effects with an atomistic resolution. This can be achieved by self-consistently coupling molecular dynamics (MD), density-functional theory (DFT), and quantum transport (QT) simulations of both electrons and phonons.
The Computational Nanoelectronics Group of ETH Zurich recently started implementing a novel, state-of-the-art TCAD tool called QuaTrEx that can perform ab-initio QT calculations at unprecedented scale. As QuaTrEx aims to solve for the transport and interactions of various quanta (electrons, phonons, etc) directly at atomic resolution, it requires ab-initio material inputs corresponding to the simulated device components, such as the Hamiltonian and Dynamical matrices, electron-phonon coupling elements, forces and energies, etc. Computing these inputs for device-scale structures, with methods such as DFT, currently poses a bottleneck in the application's capabilities.
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. 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 GitHub.
Profile
- 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
Workplace
Workplace
We offer
- 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
We value diversity and sustainability
Curious? So are we.
We look forward to receiving your online application with the following documents:
- CV and list of publications
- Letter of motivation
- Short description of PhD thesis
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
Further information about the Computational Nanoelectronics Group can be found on our website. Questions regarding the position should be directed to Prof. Dr. Mathieu Luisier, email mluisier@iis.ee.ethz.ch (no applications).
We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.