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 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 at ETH Zurich has recently begun implementing a novel, state-of-the-art TCAD tool called QuaTrEx that can perform ab-initio QT calculations at an 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. Computing these inputs for device-scale structures with methods such as DFT currently poses a bottleneck in the application's capabilities.
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). This project officially commenced on January 1st, 2026, and will conclude on December 31st, 2029. The goal of this research effort is to apply machine learning (ML) techniques, particularly (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 includes two PhD students and will closely collaborate with the QuaTrEx 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 also allow us to produce 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 utilized in quantum transport simulations. In the future, 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 significant computational components involved 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 will span 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.
Join ETH Zurich, an institution that 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.
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 where the rights and dignity of all our staff and students are respected. Sustainability is a core value for us - we continually work towards a climate-neutral future.
We look forward to receiving your online application using the form below. Please include the following documents:
Only applications matching the job profile will be considered. For further information about the Computational Nanoelectronics Group, please visit our website. Questions regarding the position should be directed to Prof. Dr. Mathieu Luisier at mluisier@iis.ee.ethz.ch.
ETH Zurich is one of the world's leading universities specializing 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 inspires excellence. Located in the heart of Europe, we forge connections worldwide, working together to develop solutions for the global challenges of today and tomorrow.
Location : Zürich
Country : Switzerland