100%, Zurich, fixed-term
The simulation of electronic devices has a long and successful history of accompanying experimental developments, whether for transistors or memory cells. Today, for practical relevance, technology computer-aided design (TCAD) tools must operate at the ab-initio and quantum mechanical level. They should also capture the interplay between electrical (voltage-induced currents), thermal (excitation of crystal vibrations), and structural (migration of atoms) effects with atomic resolution. This can be accomplished 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 initiated the implementation of a novel, state-of-the-art TCAD tool called QuaTrEx. This tool can perform ab-initio QT calculations at unprecedented scales. 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. Currently, computing these inputs for device-scale structures using methods such as DFT presents a bottleneck in the application’s capabilities.
The Computational Nanoelectronics Group has been awarded a grant from the Swiss National Science Foundation entitled "Machine Learning for Optimized Ab-initio Quantum Transport Simulations" (MALOQ). This project officially began on January 1, 2026, and will conclude on December 31, 2029. The goal is to apply machine learning (ML) techniques, particularly (equivariant) graph neural networks, to accelerate the creation of all physical quantities necessary for ab-initio QT simulations of nanoelectronic devices. We are seeking a post-doctoral fellow to join a team comprised of two PhD students and to closely collaborate with the QuaTrEx developers.
As part of the MALOQ project, you will train state-of-the-art ML models to learn the atomic, electronic, and vibrational properties of large-scale atomic systems that form the building blocks of semiconductor devices. The goal is to predict these properties for arbitrarily large structures with DFT-level accuracy.
Your initial focus will be to extend the large-scale equivariant graph neural networks we develop for Hamiltonian matrix prediction to handle dynamical matrices. This ML framework will also enable 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 subsequently used in quantum transport simulations. In the long term, 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. This position includes a significant computational component, involving the deployment of multi-GPU codes to efficiently train on the large, densely-connected graph-structured data encountered in our systems of interest.
Your contributions will span a range of activities, from methodological development and implementation to applications within realistic semiconductor device systems comprising thousands of atoms. All codes will be made freely available to the scientific community through GitHub.
ETH Zurich is an institution that not only supports your professional development but also contributes positively to society. You can expect numerous benefits, such as public transport season tickets, car-sharing options, a wide range of sports offered by the ASVZ, childcare support, and attractive pension benefits.
ETH Zurich promotes an inclusive culture, encouraging equality of opportunity and valuing diversity. We nurture a working and learning environment that respects the rights and dignity of all staff and students. Sustainability is a core value for us as we consistently work toward a climate-neutral future.
We look forward to receiving your online application with the following documents:
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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. With over 30,000 individuals from more than 120 countries, our university fosters independent thinking and an environment that inspires excellence. Located in the heart of Europe, we forge connections globally to collaboratively develop solutions for the challenges of today and tomorrow.
Location : Zürich
Country : Switzerland