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 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 chevron_right Working, teaching and research at ETH Zurich We value diversity and sustainability In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future. 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. About ETH Zürich ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow. If you apply for this position please say you saw it on Computeroxy

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

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

chevron_right Working, teaching and research at ETH Zurich

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

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.

About ETH Zürich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

If you apply for this position please say you saw it on Computeroxy

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