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 technology to remain practically relevant, such tools for computer-aided design (TCAD) must operate at the ab-initio and quantum mechanical levels. Moreover, they should effectively capture the interplay between electrical (voltage-induced currents), thermal (excitation of crystal vibrations), and structural (migration of atoms) effects with atomistic resolution. Achieving this requires 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, which can perform ab-initio QT calculations at an unprecedented scale. As QuaTrEx aims to solve the transport and interactions of various quanta (such as electrons and phonons) directly at atomic resolution, it necessitates ab-initio material inputs corresponding to the simulated device components. These inputs include the Hamiltonian and dynamical matrices, electron-phonon coupling elements, forces, and energies. Computing these inputs for device-scale structures using methods such as DFT currently presents 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 began 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 required for 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 that represent the building blocks of semiconductor devices. The aim is to accurately predict these properties for structures of arbitrary size at the DFT-level of precision.
Your initial task will involve extending the large-scale equivariant GNNs developed for Hamiltonian matrix prediction to handle dynamical matrices. This ML framework will also enable us to generate the derivatives of both quantities, corresponding to the electron-phonon and anharmonic phonon-phonon coupling elements. Subsequently, these elements can be used to compute dedicated scattering rates for quantum transport simulations. Over time, we aim to pre-train a common GNN backbone model capable of predicting electronic, structural, and thermal quantities, while leveraging underlying symmetries for enhanced computational efficiency. The role includes significant computational components, particularly in deploying multi-GPU codes to efficiently train on the large, densely connected, and graph-structured data inherent to our systems of interest.
Your contributions will encompass methodological development, implementation, and application to realistic semiconductor device systems composed of thousands of atoms. All codes will be made freely available to the scientific community through GitHub.
ETH Zurich offers an enriching and supportive environment conducive to professional development.
ETH Zurich promotes an inclusive culture that encourages equal opportunities and values diversity. We strive to nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Sustainability is a core principle for us, as we consistently work towards a climate-neutral future.
We look forward to receiving your online application using the form below. Please include the following documents:
Please note that only applications matching the job profile will be considered.
Further information about the Computational Nanoelectronics Group can be found on our website. Questions regarding the position can be directed to Prof. Dr. Mathieu Luisier at mluisier@iis.ee.ethz.ch (no applications).
ETH Zurich is one of the world's leading universities specializing in science and technology. We are renowned for our exceptional education and cutting-edge fundamental research, along with the direct transfer of new knowledge into society. With over 30,000 individuals from more than 120 countries, our university fosters an environment that encourages independent thinking and inspires excellence. Located in the heart of Europe while forging global connections, we work together to develop solutions for the global challenges of today and tomorrow.
Location : Zürich
Country : Switzerland